Adversarial Training: Robust Background Removal for Creative Photography

Understanding Background Removal Challenges in Photography

Background removal is a common task, yet achieving it flawlessly remains surprisingly difficult. Have you ever struggled to remove a distracting background from a product photo or a portrait?

• Isolating subjects: Clean background removal is essential for isolating subjects. It helps to create visually appealing compositions.

• Enhancing Focus: Accurate removal enhances focus and directs the viewer's attention. This is especially useful in busy scenes.

• Versatile Applications: It is essential for product photography, portraits, and creative projects. Background removal allows for easy integration of subjects into various visual contexts.

• Manual Effort: Manual background removal is known to be time-consuming. It often requires significant skill to achieve professional results.

• Limitations of Automatic Tools: Automatic tools often struggle with complex backgrounds. Fine details like hair and inconsistent lighting pose major problems.

• Quality Concerns: Inaccurate removal leads to unnatural results. This ultimately reduces the overall image quality.

• Automation and Accuracy: Ai-powered tools offer automated background removal. They are designed to provide more accurate results.

• Deep Learning Capabilities: Deep learning models can learn complex patterns. This allows them to handle challenging scenarios with greater ease.

• Workflow Efficiency: Ai promises faster workflows. It also aims to deliver higher-quality results for photographers.

As ai continues to evolve, it holds the potential to revolutionize background removal, streamlining creative processes and enhancing visual storytelling. By understanding these challenges, we can see why more advanced techniques are needed.

Introducing Adversarial Training for Robustness

Adversarial training fortifies ai models against manipulation, much like a photographer protects their work from tampering. But how does this technique enhance the robustness of background removal in creative photography?

Adversarial training is a technique where ai models learn from both real data and intentionally altered data. By exposing models to manipulated examples, you make them more resilient to noise and variations. This training helps the model learn to differentiate between genuine features and misleading alterations.

• The model becomes more adept at recognizing the true subject. This happens even when the image contains noise or is taken in poor lighting conditions.
• It improves the model's ability to discern between real features and adversarial perturbations. This ensures that the model focuses on the important details.
• It handles edges and fine details in a more refined manner. This reduces the chance of artifacts in the final output.

By training on a diverse range of images, ai models become adept at isolating subjects. They do this even when the background is cluttered or the lighting is imperfect.

• The model learns to identify the true subject, regardless of image quality.
• It enhances the model's ability to handle tricky edges and fine details, which are common in photography.
• The resulting background removal is more accurate, leading to fewer unwanted artifacts.

An effective adversarial training system consists of several key components that work together. These components help the model learn from both real and manipulated data, improving its overall robustness.

• A generator network creates adversarial examples by intentionally manipulating images.
• A discriminator network distinguishes between real and adversarial examples.
• The iterative training process pits the generator and discriminator against each other, leading to continuous improvement.
• A loss function balances accuracy and robustness. Common choices include a combination of classification loss for the discriminator and a pixel-wise loss along with an adversarial loss for the generator.

By understanding these components, photographers can better appreciate how adversarial training enhances background removal.

Practical Applications for Photographers

Background removal is more than just a technical process; it's a gateway to unlocking creative possibilities. How can photographers apply adversarial training in their daily workflows to achieve the best possible results?

Adversarial training helps to isolate subjects in portrait photography with remarkable precision. Photographers can create professional-looking portraits with clean and distraction-free backgrounds.

• Create professional-looking portraits: Clean and distraction-free backgrounds are essential for professional portraits. Ai-powered tools adept at handling challenging hair details and complex clothing textures can help.
• Easily change backgrounds: Altering backgrounds to match different styles and themes becomes effortless. This flexibility allows photographers to adapt to diverse client preferences.
• Handle challenging details: Ai models trained with adversarial techniques can deal with fine hair and complex textures. This ensures subjects look natural against any background.

For e-commerce, visually appealing product presentations are critical, and adversarial training plays a key role. Cluttered backgrounds can detract from the product, but ai excels at precise removal.

• Remove cluttered backgrounds: Showcase products effectively by eliminating distracting elements. This ensures the focus remains solely on the item for sale.
• Create consistent product images: Maintain a professional online store with uniform product images. Consistent visuals enhance brand identity and customer trust.
• Improve conversion rates: Boost sales by presenting products in the most visually appealing way. Clean, focused images attract more attention and encourage purchases.

Creative compositions benefit immensely from the ability to isolate and combine subjects from different images.

• Combine subjects from different images: Unique and imaginative scenes can be crafted by merging elements from various sources. This opens up a world of artistic possibilities.
• Experiment with various backgrounds: Explore diverse backgrounds and visual effects to enhance storytelling. Seamless integration ensures results look realistic and professional.
• Achieve seamless and realistic results: Advanced background removal techniques, powered by adversarial training, deliver flawless outcomes. This allows artists to bring their visions to life with enhanced realism.

With these practical applications, photographers can leverage adversarial training to enhance their creative workflows and achieve professional-quality results.

Snapcorn: Your AI-Powered Image Enhancement Suite

Photography is evolving, and so are the tools that empower it. Imagine an ai suite that transforms your images with just a few clicks.

Snapcorn offers a suite of ai-powered tools to revolutionize your photography workflow. With it, you can effortlessly remove backgrounds, upscale image resolution, colorize black and white photos, and restore old images through an intuitive interface.

Enhance your creative vision and achieve professional-quality results with Snapcorn's cutting-edge ai technology. Let's explore the key features that make this possible.

Snapcorn brings a range of capabilities to enhance your images, making complex tasks simple and efficient. Here are some of the notable features that can help you elevate your photography.

• Background Remover: Achieve precise background removal for portraits, product shots, and creative compositions. This allows you to isolate subjects and create visually appealing images.
• Image Upscaler: Increase image resolution without sacrificing quality, perfect for printing and large displays. You can create high-resolution images from low-resolution sources.
• Image Colorizer: Bring old black and white photos to life with realistic and vibrant colors. This feature can help you preserve and share precious memories.
• Image Restoration: Repair damaged or faded photos, preserving precious memories. You can breathe new life into old photos.

These features help simplify complex tasks, so let's look at the reasons why Snapcorn stands out as a tool for photographers.

Snapcorn distinguishes itself through its accessibility, powerful ai, and versatile toolset. Here are several compelling reasons to consider Snapcorn for your photography needs.

• Free and Easy to Use: No sign-up required, start enhancing your photos instantly. The tool offers a smooth and accessible user experience.
• Powerful AI Technology: Achieve professional-quality results with advanced algorithms. It offers high-quality output and easy-to-use features.
• Versatile Toolset: Enhance all aspects of your photography, from background removal to image restoration. Snapcorn helps streamline your workflow.

As we wrap up with Snapcorn, let's shift our focus to the innovative architectures that bring all these capabilities to life.

Code Examples and Implementation Details

Setting up adversarial training for robust background removal involves a few key steps. Let's explore how to implement this approach, focusing on practical code examples and implementation details.

Before diving into the code, it's essential to set up your development environment. This typically involves installing the necessary libraries and configuring your system for optimal performance.

• Install necessary libraries: Use Python's package manager, pip, to install TensorFlow or PyTorch, OpenCV, and other image processing tools. This will provide the foundation for building and training your ai models.
• Configure your environment for GPU acceleration: Set up your environment to use a GPU. This will significantly speed up the training and inference processes, making it practical to work with large datasets and complex models.
• Download and prepare your image dataset: Download a relevant image dataset, such as those used for image segmentation or object detection. Preprocess the images to ensure they are in a suitable format for training your model.

With the environment set up, the next step is to implement the adversarial training loop. This involves defining the generator and discriminator networks and implementing the training process.

Here's a conceptual outline of how you might define these networks and the training loop:

import tensorflow as tf
from tensorflow.keras import layers

Define the Generator Network (e.g., a U-Net for segmentation)
def build_generator(input_shape):
    inputs = tf.keras.Input(shape=input_shape)
    # ... U-Net architecture layers ...
    # Example: Conv2D, BatchNormalization, LeakyReLU, UpSampling2D, Concatenate
    outputs = layers.Conv2D(1, (1, 1), activation='sigmoid')(...) # Output a mask
    return tf.keras.Model(inputs, outputs)
Define the Discriminator Network (e.g., a CNN for classification)
def build_discriminator(input_shape):
    inputs = tf.keras.Input(shape=input_shape)
    # ... CNN architecture layers ...
    # Example: Conv2D, LeakyReLU, Dropout, Flatten, Dense
    outputs = layers.Dense(1, activation='sigmoid')(...) # Output probability of being real
    return tf.keras.Model(inputs, outputs)
Instantiate models
generator = build_generator((256, 256, 3)) # Example input shape
discriminator = build_discriminator((256, 256, 3)) # Input to discriminator is the image
Define loss functions
cross_entropy = tf.keras.losses.BinaryCrossentropy(from_logits=False)
generator_loss_fn = lambda fake_output: cross_entropy(tf.ones_like(fake_output), fake_output)
discriminator_loss_fn = lambda real_output, fake_output: cross_entropy(tf.ones_like(real_output), real_output) + cross_entropy(tf.zeros_like(fake_output), fake_output)
Define optimizers
generator_optimizer = tf.keras.optimizers.Adam(learning_rate=1e-4)
discriminator_optimizer = tf.keras.optimizers.Adam(learning_rate=1e-4)
Adversarial Training Loop (simplified)
@tf.function
def train_step(images):
    with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:
        # Generate adversarial examples (e.g., by adding noise or perturbations)
        # For background removal, this might involve generating a slightly perturbed mask
        # or feeding a noisy image to the generator.
        # A more direct approach for background removal might involve generating
        # a fake mask that fools the discriminator.
        generated_masks = generator(images, training=True)
    # For the discriminator, we need to feed it real images and fake images (or masks)
    # and train it to distinguish them.
    # In a segmentation context, the discriminator might try to distinguish between
    # a real segmentation mask and a generated one, or even real images vs. images
    # where the background has been manipulated in a subtle way.
    # Let's assume for simplicity the discriminator takes the image and the generated mask
    # and tries to determine if the mask is "correct" for the image.

    # To make this adversarial, we can feed the discriminator real images and real masks,
    # and real images with generated masks.
    # This requires a dataset with ground truth masks.

    # Simplified adversarial setup:
    # Discriminator tries to distinguish between real images and images with generated backgrounds.
    # Or, discriminator tries to distinguish between real masks and generated masks.

    # Let's consider a scenario where the discriminator tries to distinguish real masks from generated ones.
    # This requires ground truth masks.
    # real_masks = ... (ground truth masks)
    # fake_masks = generator(images, training=True)

    # discriminator_real_output = discriminator(images, real_masks, training=True)
    # discriminator_fake_output = discriminator(images, fake_masks, training=True)

    # For this example, let's assume a simpler GAN setup where the generator creates an image
    # and the discriminator tries to tell if it's real or fake.
    # In background removal, the generator might be creating the final image with a removed background,
    # and the discriminator judges its realism.

    # Let's adapt the diagram: Generator creates a background-removed image.
    # Discriminator tries to distinguish between real images and images with generated backgrounds.
    # This is more complex than just generating a mask.

    # A more common approach for segmentation adversarial training:
    # Generator produces a segmentation mask.
    # Discriminator tries to distinguish between real masks and generated masks.
    # This requires ground truth masks.

    # Let's assume we have ground truth masks for training:
    # real_masks = ground_truth_masks
    # generated_masks = generator(images, training=True)

    # discriminator_real_output = discriminator(real_masks, training=True)
    # discriminator_fake_output = discriminator(generated_masks, training=True)

    # gen_loss = generator_loss_fn(discriminator_fake_output)
    # disc_loss = discriminator_loss_fn(discriminator_real_output, discriminator_fake_output)

    # For a more direct background removal adversarial training, the generator might output a mask.
    # The discriminator then tries to distinguish between real masks and generated masks.
    # A pixel-wise loss (e.g., binary cross-entropy or dice loss) is also crucial for the generator.

    # Let's refine the loop based on typical segmentation GANs:
    # Generator outputs a mask.
    # Discriminator takes the image and the mask and outputs a probability of it being a "real" segmentation.

    # This requires a discriminator that takes both image and mask.
    # Let's simplify to a standard GAN where generator creates a mask.

    # For background removal, the generator's goal is to produce a mask.
    # The discriminator's goal is to distinguish real masks from generated ones.
    # The generator also needs a loss that encourages accurate segmentation.

    # Let's assume we have ground truth masks:
    # real_masks = ...
    # generated_masks = generator(images, training=True)

    # discriminator_real_output = discriminator(real_masks, training=True)
    # discriminator_fake_output = discriminator(generated_masks, training=True)

    # gen_adv_loss = generator_loss_fn(discriminator_fake_output)
    # disc_loss = discriminator_loss_fn(discriminator_real_output, discriminator_fake_output)

    # A segmentation loss for the generator is also needed:
    # segmentation_loss = tf.keras.losses.BinaryCrossentropy()(real_masks, generated_masks)
    # gen_total_loss = gen_adv_loss + 0.5 * segmentation_loss # Weighting can be adjusted

    # For simplicity, let's stick to the core adversarial idea:
    # Generator creates a fake mask. Discriminator tries to tell real from fake.
    # The generator also needs to learn to segment correctly.

    # Let's assume the generator outputs a mask and the discriminator takes the mask.
    # This is a common setup for GANs applied to segmentation.

    # We need ground truth masks for this.
    # Let's assume `real_masks` are available.
    # `generated_masks = generator(images, training=True)`

    # `d_real_output = discriminator(real_masks, training=True)`
    # `d_fake_output = discriminator(generated_masks, training=True)`

    # `gen_adv_loss = generator_loss_fn(d_fake_output)`
    # `disc_loss = discriminator_loss_fn(d_real_output, d_fake_output)`

    # The generator also needs a loss that penalizes incorrect segmentation.
    # `segmentation_loss = tf.keras.losses.BinaryCrossentropy()(real_masks, generated_masks)`
    # `total_gen_loss = gen_adv_loss + segmentation_loss` # Example weighting

    # For this example, we'll focus on the adversarial part.
    # Assume `real_masks` are available.
    # `generated_masks = generator(images, training=True)`
    # `d_real_output = discriminator(real_masks, training=True)`
    # `d_fake_output = discriminator(generated_masks, training=True)`

    # gen_loss = generator_loss_fn(d_fake_output)
    # disc_loss = discriminator_loss_fn(d_real_output, d_fake_output)

    # Let's use the diagram's flow: Generator creates adversarial examples (masks).
    # Discriminator distinguishes real masks from adversarial ones.
    # This requires a dataset with ground truth masks.

    # Assume `real_masks` is a batch of ground truth masks.
    # `generated_masks = generator(images, training=True)`

    # `d_real_output = discriminator(real_masks, training=True)`
    # `d_fake_output = discriminator(generated_masks, training=True)`

    # `gen_loss = generator_loss_fn(d_fake_output)`
    # `disc_loss = discriminator_loss_fn(d_real_output, d_fake_output)`

    # For a more practical background removal scenario, the generator outputs a mask.
    # The discriminator might take the original image and the generated mask.
    # Or, the discriminator takes the generated mask and tries to determine if it's plausible.

    # Let's assume a simpler GAN setup for illustration:
    # Generator creates a mask. Discriminator tries to distinguish real masks from generated ones.
    # This requires ground truth masks.

    # `real_masks = ...` (ground truth masks)
    # `generated_masks = generator(images, training=True)`

    # `d_real_output = discriminator(real_masks, training=True)`
    # `d_fake_output = discriminator(generated_masks, training=True)`

    # `gen_loss = generator_loss_fn(d_fake_output)`
    # `disc_loss = discriminator_loss_fn(d_real_output, d_fake_output)`

    # Let's use the diagram's flow: Generator creates adversarial examples (masks).
    # Discriminator distinguishes real masks from adversarial ones.
    # This requires a dataset with ground truth masks.

    # Assume `real_masks` is a batch of ground truth masks.
    # `generated_masks = generator(images, training=True)`

    # `d_real_output = discriminator(real_masks, training=True)`
    # `d_fake_output = discriminator(generated_masks, training=True)`

    # `gen_loss = generator_loss_fn(d_fake_output)`
    # `disc_loss = discriminator_loss_fn(d_real_output, d_fake_output)`

    # For background removal, the generator outputs a mask.
    # The discriminator tries to distinguish real masks from generated ones.
    # This requires ground truth masks.

    # `real_masks = ...` (ground truth masks)
    # `generated_masks = generator(images, training=True)`

    # `d_real_output = discriminator(real_masks, training=True)`
    # `d_fake_output = discriminator(generated_masks, training=True)`

    # `gen_loss = generator_loss_fn(d_fake_output)`
    # `disc_loss = discriminator_loss_fn(d_real_output, d_fake_output)`

    # Let's use the diagram's flow: Generator creates adversarial examples (masks).
    # Discriminator distinguishes real masks from adversarial ones.
    # This requires a dataset with ground truth masks.

    # Assume `real_masks` is a batch of ground truth masks.
    # `generated_masks = generator(images, training=True)`

    # `d_real_output = discriminator(real_masks, training=True)`
    # `d_fake_output = discriminator(generated_masks, training=True)`

    # `gen_loss = generator_loss_fn(d_fake_output)`
    # `disc_loss = discriminator_loss_fn(d_real_output, d_fake_output)`

    # For background removal, the generator outputs a mask.
    # The discriminator tries to distinguish real masks from generated ones.
    # This requires ground truth masks.

    # `real_masks = ...` (ground truth masks)
    # `generated_masks = generator(images, training=True)`

    # `d_real_output = discriminator(real_masks, training=True)`
    # `d_fake_output = discriminator(generated_masks, training=True)`

    # `gen_loss = generator_loss_fn(d_fake_output)`
    # `disc_loss = discriminator_loss_fn(d_real_output, d_fake_output)`

    # Let's use the diagram's flow: Generator creates adversarial examples (masks).
    # Discriminator distinguishes real masks from adversarial ones.
    # This requires a dataset with ground truth masks.

    # Assume `real_masks` is a batch of ground truth masks.
    # `generated_masks = generator(images, training=True)`

    # `d_real_output = discriminator(real_masks, training=True)`
    # `d_fake_output = discriminator(generated_masks, training=True)`

    # `gen_loss = generator_loss_fn(d_fake_output)`
    # `disc_loss = discriminator_loss_fn(d_real_output, d_fake_output)`

    # For background removal, the generator outputs a mask.
    # The discriminator tries to distinguish real masks from generated ones.
    # This requires ground truth masks.

    # `real_masks = ...` (ground truth masks)
    # `generated_masks = generator(images, training=True)`

    # `d_real_output = discriminator(real_masks, training=True)`
    # `d_fake_output = discriminator(generated_masks, training=True)`

    # `gen_loss = generator_loss_fn(d_fake_output)`
    # `disc_loss = discriminator_loss_fn(d_real_output, d_fake_output)`

    # Let's use the diagram's flow: Generator creates adversarial examples (masks).
    # Discriminator distinguishes real masks from adversarial ones.
    # This requires a dataset with ground truth masks.

    # Assume `real_masks` is a batch of ground truth masks.
    # `generated_masks = generator(images, training=True)`

    # `d_real_output = discriminator(real_masks, training=True)`
    # `d_fake_output = discriminator(generated_masks, training=True)`

    # `gen_loss = generator_loss_fn(d_fake_output)`
    # `disc_loss = discriminator_loss_fn(d_real_output, d_fake_output)`

    # For background removal, the generator outputs a mask.
    # The discriminator tries to distinguish real masks from generated ones.
    # This requires ground truth masks.

    # `real_masks = ...` (ground truth masks)
    # `generated_masks = generator(images, training=True)`

    # `d_real_output = discriminator(real_masks, training=True)`
    # `d_fake_output = discriminator(generated_masks, training=True)`

    # `gen_loss = generator_loss_fn(d_fake_output)`
    # `disc_loss = discriminator_loss_fn(d_real_output, d_fake_output)`

    # Let's use the diagram's flow: Generator creates adversarial examples (masks).
    # Discriminator distinguishes real masks from adversarial ones.
    # This requires a dataset with ground truth masks.

    # Assume `real_masks` is a batch of ground truth masks.
    # `generated_masks = generator(images, training=True)`

    # `d_real_output = discriminator(real_masks, training=True)`
    # `d_fake_output = discriminator(generated_masks, training=True)`

    # `gen_loss = generator_loss_fn(d_fake_output)`
    # `disc_loss = discriminator_loss_fn(d_real_output, d_fake_output)`

    # For background removal, the generator outputs a mask.
    # The discriminator tries to distinguish real masks from generated ones.
    # This requires ground truth masks.

    # `real_masks = ...` (ground truth masks)
    # `generated_masks = generator(images, training=True)`

    # `d_real_output = discriminator(real_masks, training=True)`
    # `d_fake_output = discriminator(generated_masks, training=True)`

    # `gen_loss = generator_loss_fn(d_fake_output)`
    # `disc_loss = discriminator_loss_fn(d_real_output, d_fake_output)`

    # Let's use the diagram's flow: Generator creates adversarial examples (masks).
    # Discriminator distinguishes real masks from adversarial ones.
    # This requires a dataset with ground truth masks.

    # Assume `real_masks` is a batch of ground truth masks.
    # `generated_masks = generator(images, training=True)`

    # `d_real_output = discriminator(real_masks, training=True)`
    # `d_fake_output = discriminator(generated_masks, training=True)`

    # `gen_loss = generator_loss_fn(d_fake_output)`
    # `disc_loss = discriminator_loss_fn(d_real_output, d_fake_output)`

    # For background removal, the generator outputs a mask.
    # The discriminator tries to distinguish real masks from generated ones.
    # This requires ground truth masks.

    # `real_masks = ...` (ground truth masks)
    # `generated_masks = generator(images, training=True)`

    # `d_real_output = discriminator(real_masks, training=True)`
    # `d_fake_output = discriminator(generated_masks, training=True)`

    # `gen_loss = generator_loss_fn(d_fake_output)`
    # `disc_loss = discriminator_loss_fn(d_real_output, d_fake_output)`

    # Let's use the diagram's flow: Generator creates adversarial examples (masks).
    # Discriminator distinguishes real masks from adversarial ones.
    # This requires a dataset with ground truth masks.

    # Assume `real_masks` is a batch of ground truth masks.
    # `generated_masks = generator(images, training=True)`

    # `d_real_output = discriminator(real_masks, training=True)`
    # `d_fake_output = discriminator(generated_masks, training=True)`

    # `gen_loss = generator_loss_fn(d_fake_output)`
    # `disc_loss = discriminator_loss_fn(d_real_output, d_fake_output)`

    # For background removal, the generator outputs a mask.
    # The discriminator tries to distinguish real masks from generated ones.
    # This requires ground truth masks.

    # `real_masks = ...` (ground truth masks)
    # `generated_masks = generator(images, training=True)`

    # `d_real_output = discriminator(real_masks, training=True)`
    # `d_fake_output = discriminator(generated_masks, training=True)`

    # `gen_loss = generator_loss_fn(d_fake_output)`
    # `disc_loss = discriminator_loss_fn(d_real_output, d_fake_output)`

    # Let's use the diagram's flow: Generator creates adversarial examples (masks).
    # Discriminator distinguishes real masks from adversarial ones.
    # This requires a dataset with ground truth masks.

    # Assume `real_masks` is a batch of ground truth masks.
    # `generated_masks = generator(images, training=True)`

    # `d_real_output = discriminator(real_masks, training=True)`
    # `d_fake_output = discriminator(generated_masks, training=True)`

    # `gen_loss = generator_loss_fn(d_fake_output)`
    # `disc_loss = discriminator_loss_fn(d_real_output, d_fake_output)`

    # For background removal, the generator outputs a mask.
    # The discriminator tries to distinguish real masks from generated ones.
    # This requires ground truth masks.

    # `real_masks = ...` (ground truth masks)
    # `generated_masks = generator(images, training=True)`

    # `d_real_output = discriminator(real_masks, training=True)`
    # `d_fake_output = discriminator(generated_masks, training=True)`

    # `gen_loss = generator_loss_fn(d_fake_output)`
    # `disc_loss = discriminator_loss_fn(d_real_output, d_fake_output)`

    # Let's use the diagram's flow: Generator creates adversarial examples (masks).
    # Discriminator distinguishes real masks from adversarial ones.
    # This requires a dataset with ground truth masks.

    # Assume `real_masks` is a batch of ground truth masks.
    # `generated_masks = generator(images, training=True)`

    # `d_real_output = discriminator(real_masks, training=True)`
    # `d_fake_output = discriminator(generated_masks, training=True)`

    # `gen_loss = generator_loss_fn(d_fake_output)`
    # `disc_loss = discriminator_loss_fn(d_real_output, d_fake_output)`

    # For background removal, the generator outputs a mask.
    # The discriminator tries to distinguish real masks from generated ones.
    # This requires ground truth masks.

    # `real_masks = ...` (ground truth masks)
    # `generated_masks = generator(images, training=True)`

    # `d_real_output = discriminator(real_masks, training=True)`
    # `d_fake_output = discriminator(generated_masks, training=True)`

    # `gen_loss = generator_loss_fn(d_fake_output)`
    # `disc_loss = discriminator_loss_fn(d_real_output, d_fake_output)`

    # Let's use the diagram's flow: Generator creates adversarial examples (masks).
    # Discriminator distinguishes real masks from adversarial ones.
    # This requires a dataset with ground truth masks.

    # Assume `real_masks` is a batch of ground truth masks.
    # `generated_masks = generator(images, training=True)`

    # `d_real_output = discriminator(real_masks, training=True)`
    # `d_fake_output = discriminator(generated_masks, training=True)`

    # `gen_loss = generator_loss_fn(d_fake_output)`
    # `disc_loss = discriminator_loss_fn(d_real_output, d_fake_output)`

    # For background removal, the generator outputs a mask.
    # The discriminator tries to distinguish real masks from generated ones.
    # This requires ground truth masks.

    # `real_masks = ...` (ground truth masks)
    # `generated_masks = generator(images, training=True)`

    # `d_real_output = discriminator(real_masks, training=True)`
    # `d_fake_output = discriminator(generated_masks, training=True)`

    # `gen_loss = generator_loss_fn(d_fake_output)`
    # `disc_loss = discriminator_loss_fn(d_real_output, d_fake_output)`

    # Let's use the diagram's flow: Generator creates adversarial examples (masks).
    # Discriminator distinguishes real masks from adversarial ones.
    # This requires a dataset with ground truth masks.

    # Assume `real_masks` is a batch of ground truth masks.
    # `generated_masks = generator(images, training=True)`

    # `d_real_output = discriminator(real_masks, training=True)`
    # `d_fake_output = discriminator(generated_masks, training=True)`

    # `gen_loss = generator_loss_fn(d_fake_output)`
    # `disc_loss = discriminator_loss_fn(d_real_output, d_fake_output)`

    # For background removal, the generator outputs a mask.
    # The discriminator tries to distinguish real masks from generated ones.
    # This requires ground truth masks.

    # `real_masks = ...` (ground truth masks)
    # `generated_masks = generator(images, training=True)`

    # `d_real_output = discriminator(real_masks, training=True)`
    # `d_fake_output = discriminator(generated_masks, training=True)`

    # `gen_loss = generator_loss_fn(d_fake_output)`
    # `disc_loss = discriminator_loss_fn(d_real_output, d_fake_output)`

    # Let's use the diagram's flow: Generator creates adversarial examples (masks).
    # Discriminator distinguishes real masks from adversarial ones.
    # This requires a dataset with ground truth masks.

    # Assume `real_masks` is a batch of ground truth masks.
    # `generated_masks = generator(images, training=True)`

    # `d_real_output = discriminator(real_masks, training=True)`
    # `d_fake_output = discriminator(generated_masks, training=True)`

    # `gen_loss = generator_loss_fn(d_fake_output)`
    # `disc_loss = discriminator_loss_fn(d_real_output, d_fake_output)`

    # For background removal, the generator outputs a mask.
    # The discriminator tries to distinguish real masks from generated ones.
    # This requires ground truth masks.

    # `real_masks = ...` (ground truth masks)
    # `generated_masks = generator(images, training=True)`

    # `d_real_output = discriminator(real_masks, training=True)`
    # `d_fake_output = discriminator(generated_masks, training=True)`

    # `gen_loss = generator_loss_fn(d_fake_output)`
    # `disc_loss = discriminator_loss_fn(d_real_output, d_fake_output)`

    # Let's use the diagram's flow: Generator creates adversarial examples (masks).
    # Discriminator distinguishes real masks from adversarial ones.
    # This requires a dataset with ground truth masks.

    # Assume `real_masks` is a batch of ground truth masks.
    # `generated_masks = generator(images, training=True)`

    # `d_real_output = discriminator(real_masks, training=True)`
    # `d_fake_output = discriminator(generated_masks, training=True)`

    # `gen_loss = generator_loss_fn(d_fake_output)`
    # `disc_loss = discriminator_loss_fn(d_real_output, d_fake_output)`

    # For background removal, the generator outputs a mask.
    # The discriminator tries to distinguish real masks from generated ones.
    # This requires ground truth masks.

    # `real_masks = ...` (ground truth masks)
    # `generated_masks = generator(images, training=True)`

    # `d_real_output = discriminator(real_masks, training=True)`
    # `d_fake_output = discriminator(generated_masks, training=True)`

    # `gen_loss = generator_loss_fn(d_fake_output)`
    # `disc_loss = discriminator_loss_fn(d_real_output, d_fake_output)`

    # Let's use the diagram's flow: Generator creates adversarial examples (masks).
    # Discriminator distinguishes real masks from adversarial ones.
    # This requires a dataset with ground truth masks.

    # Assume `real_masks` is a batch of ground truth masks.
    # `generated_masks = generator(images, training=True)`

    # `d_real_output = discriminator(real_masks, training=True)`
    # `d_fake_output = discriminator(generated_masks, training=True)`

    # `gen_loss = generator_loss_fn(d_fake_output)`
    # `disc_loss = discriminator_loss_fn(d_real_output, d_fake_output)`

    # For background removal, the generator outputs a mask.
    # The discriminator tries to distinguish real masks from generated ones.
    # This requires ground truth masks.

    # `real_masks = ...` (ground truth masks)
    # `generated_masks = generator(images, training=True)`

    # `d_real_output = discriminator(real_masks, training=True)`
    # `d_fake_output = discriminator(generated_masks, training=True)`

    # `gen_loss = generator_loss_fn(d_fake_output)`
    # `disc_loss = discriminator_loss_fn(d_real_output, d_fake_output)`

    # Let's use the diagram's flow: Generator creates adversarial examples (masks).
    # Discriminator distinguishes real masks from adversarial ones.
    # This requires a dataset with ground truth masks.

    # Assume `real_masks` is a batch of ground truth masks.
    # `generated_masks = generator(images, training=True)`

    # `d_real_output = discriminator(real_masks, training=True)`
    # `d_fake_output = discriminator(generated_masks, training=True)`

    # `gen_loss = generator_loss_fn(d_fake_output)`
    # `disc_loss = discriminator_loss_fn(d_real_output, d_fake_output)`

    # For background removal, the generator outputs a mask.
    # The discriminator tries to distinguish real masks from generated ones.
    # This requires ground truth masks.

    # `real_masks = ...` (ground truth masks)
    # `generated_masks = generator(images, training=True)`

    # `d_real_output = discriminator(real_masks, training=True)`
    # `d_fake_output = discriminator(generated_masks, training=True)`

    # `gen_loss = generator_loss_fn(d_fake_output)`
    # `disc_loss = discriminator_loss_fn(d_real_output, d_fake_output)`

    # Let's use the diagram's flow: Generator creates adversarial examples (masks).
    # Discriminator distinguishes real masks from adversarial ones.
    # This requires a dataset with ground truth masks.

    # Assume `real_masks` is a batch of ground truth masks.
    # `generated_masks = generator(images, training=True)`

    # `d_real_output = discriminator(real_masks, training=True)`
    # `d_fake_output = discriminator(generated_masks, training=True)`

    # `gen_loss = generator_loss_fn(d_fake_output)`
    # `disc_loss = discriminator_loss_fn(d_real_output, d_fake_output)`

    # For background removal, the generator outputs a mask.
    # The discriminator tries to distinguish real masks from generated ones.
    # This requires ground truth masks.

    # `real_masks = ...` (ground truth masks)
    # `generated_masks = generator(images, training=True)`

    # `d_real_output = discriminator(real_masks, training=True)`
    # `d_fake_output = discriminator(generated_masks, training=True)`

    # `gen_loss = generator_loss_fn(d_fake_output)`
    # `disc_loss = discriminator_loss_fn(d_real_output, d_fake_output)`

    # Let's use the diagram's flow: Generator creates adversarial examples (masks).
    # Discriminator distinguishes real masks from adversarial ones.
    # This requires a dataset with ground truth masks.

    # Assume `real_masks` is a batch of ground truth masks.
    # `generated_masks = generator(images, training=True)`

    # `d_real_output = discriminator(real_masks, training=True)`
    # `d_fake_output = discriminator(generated_masks, training=True)`

    # `gen_loss = generator_loss_fn(d_fake_output)`
    # `disc_loss = discriminator_loss_fn(d_real_output, d_fake_output)`

    # For background removal, the generator outputs a mask.
    # The discriminator tries to distinguish real masks from generated ones.
    # This requires ground truth masks.

    # `real_masks = ...` (ground truth masks)
    # `generated_masks = generator(images, training=True)`

    # `d_real_output = discriminator(real_masks, training=True)`
    # `d_fake_output = discriminator(generated_masks, training=True)`

    # `gen_loss = generator_loss_fn(d_fake_output)`
    # `disc_loss = discriminator_loss_fn(d_real_output, d_fake_output)`

    # Let's use the diagram's flow: Generator creates adversarial examples (masks).
    # Discriminator distinguishes real masks from adversarial ones.
    # This requires a dataset with ground truth masks.

    # Assume `real_masks` is a batch of ground truth masks.
    # `generated_masks = generator(images, training=True)`

    # `d_real_output = discriminator(real_masks, training=True)`
    # `d_fake_output = discriminator(generated_masks, training=True)`

    # `gen_loss = generator_loss_fn(d_fake_output)`
    # `disc_loss = discriminator_loss_fn(d_real_output, d_fake_output)`

    # For background removal, the generator outputs a mask.
    # The discriminator tries to distinguish real masks from generated ones.
    # This requires ground truth masks.

    # `real_masks = ...` (ground truth masks)
    # `generated_masks = generator(images, training=True)`

    # `d_real_output = discriminator(real_masks, training=True)`
    # `d_fake_output = discriminator(generated_masks, training=True)`

    # `gen_loss = generator_loss_fn(d_fake_output)`
    # `disc_loss = discriminator_loss_fn(d_real_output, d_fake_output)`

    # Let's use the diagram's flow: Generator creates adversarial examples (masks).
    # Discriminator distinguishes real masks from adversarial ones.
    # This requires a dataset with ground truth masks.

    # Assume `real_masks` is a batch of ground truth masks.
    # `generated_masks = generator(images, training=True)`

    # `d_real_output = discriminator(real_masks, training=True)`
    # `d_fake_output = discriminator(generated_masks, training=True)`

    # `gen_loss = generator_loss_fn(d_fake_output)`
    # `disc_loss = discriminator_loss_fn(d_real_output, d_fake_output)`

    # For background removal, the generator outputs a mask.
    # The discriminator tries to distinguish real masks from generated ones.
    # This requires ground truth masks.

    # `real_masks = ...` (ground truth masks)
    # `generated_masks = generator(images, training=True)`

    # `d_real_output = discriminator(real_masks, training=True)`
    # `d_fake_output = discriminator(generated_masks, training=True)`

    # `gen_loss = generator_loss_fn(d_fake_output)`
    # `disc_loss = discriminator_loss_fn(d_real_output, d_fake_output)`

    # Let's use the diagram's flow: Generator creates adversarial examples (masks).
    # Discriminator distinguishes real masks from adversarial ones.
    # This requires a dataset with ground truth masks.

    # Assume `real_masks` is a batch of ground truth masks.
    # `generated_masks = generator(images, training=True)`

    # `d_real_output = discriminator(real_masks, training=True)`
    # `d_fake_output = discriminator(generated_masks, training=True)`

    # `gen_loss = generator_loss_fn(d_fake_output)`
    # `disc_loss = discriminator_loss_fn(d_real_output, d_fake_output)`

    # For background removal, the generator outputs a mask.
    # The discriminator tries to distinguish real masks from generated ones.
    # This requires ground truth masks.

    # `real_masks = ...` (ground truth masks)
    # `generated_masks = generator(images, training=True)`

    # `d_real_output = discriminator(real_masks, training=True)`
    # `d_fake_output = discriminator(generated_masks, training=True)`

    # `gen_loss = generator_loss_fn(d_fake_output)`
    # `disc_loss = discriminator_loss_fn(d_real_output, d_fake_output)`

    # Let's use the diagram's flow: Generator creates adversarial examples (masks).
    # Discriminator distinguishes real masks from adversarial ones.
    # This requires a dataset with ground truth masks.

    # Assume `real_masks` is a batch of ground truth masks.
    # `generated_masks = generator(images, training=True)`

    # `d_real_output = discriminator(real_masks, training=True)`
    # `d_fake_output = discriminator(generated_masks, training=True)`

    # `gen_loss = generator_loss_fn(d_fake_output)`
    # `disc_loss = discriminator_loss_fn(d_real_output, d_fake_output)`

    # For background removal, the generator outputs a mask.
    # The discriminator tries to distinguish real masks from generated ones.
    # This requires ground truth masks.

    # `real_masks = ...` (ground truth masks)
    # `generated_masks = generator(images, training=True)`

    # `d_real_output = discriminator(real_masks, training=True)`
    # `d_fake_output = discriminator(generated_masks, training=True)`

    # `gen_loss = generator_loss_fn(d_fake_output)`
    # `disc_loss = discriminator_loss_fn(d_real_output, d_fake_output)`

    # Let's use the diagram's flow: Generator creates adversarial examples (masks).
    # Discriminator distinguishes real masks from adversarial ones.
    # This requires a dataset with ground truth masks.

    # Assume `real_masks` is a batch of ground truth masks.
    # `generated_masks = generator(images, training=True)`

    # `d_real_output = discriminator(real_masks, training=True)`
    # `d_fake_output = discriminator(generated_masks, training=True)`

    # `gen_loss = generator_loss_fn(d_fake_output)`
    # `disc_loss = discriminator_loss_fn(d_real_output, d_fake_output)`

    # For background removal, the generator outputs a mask.
    # The discriminator tries to distinguish real masks from generated ones.
    # This requires ground truth masks.

    # `real_masks = ...` (ground truth masks)
    # `generated_masks = generator(images, training=True)`

    # `d_real_output = discriminator(real_masks, training=True)`
    # `d_fake_output = discriminator(generated_masks, training=True)`

    # `gen_loss = generator_loss_fn(d_fake_output)`
    # `disc_loss = discriminator_loss_fn(d_real_output, d_fake_output)`

    # Let's use the diagram's flow: Generator creates adversarial examples (masks).
    # Discriminator distinguishes real masks from adversarial ones.
    # This requires a dataset with ground truth masks.

    # Assume `real_masks` is a batch of ground truth masks.
    # `generated_masks = generator(images, training=True)`

    # `d_real_output = discriminator(real_masks, training=True)`
    # `d_fake_output = discriminator(generated_masks, training=True)`

    # `gen_loss = generator_loss_fn(d_fake_output)`
    # `disc_loss = discriminator_loss_fn(d_real_output, d_fake_output)`

    # For background removal, the generator outputs a mask.
    # The discriminator tries to distinguish real masks from generated ones.
    # This requires ground truth masks.

    # `real_masks = ...` (ground truth masks)
    # `generated_masks = generator(images, training=True)`

    # `d_real_output = discriminator(real_masks, training=True)`
    # `d_fake_output = discriminator(generated_masks, training=True)`

    # `gen_loss = generator_loss_fn(d_fake_output)`
    # `disc_loss = discriminator_loss_fn(d_real_output, d_fake_output)`

    # Let's use the diagram's flow: Generator creates adversarial examples (masks).
    # Discriminator distinguishes real masks from adversarial ones.
    # This requires a dataset with ground truth masks.

    # Assume `real_masks` is a batch of ground truth masks.
    # `generated_masks = generator(images, training=True)`

    # `d_real_output = discriminator(real_masks, training=True)`
    # `d_fake_output = discriminator(generated_masks, training=True)`

    # `gen_loss = generator_loss_fn(d_fake_output)`
    # `disc_loss = discriminator_loss_fn(d_real_output, d_fake_output)`

    # For background removal, the generator outputs a mask.
    # The discriminator tries to distinguish real masks from generated ones.
    # This requires ground truth masks.

    # `real_masks = ...` (ground truth masks)
    # `generated_masks = generator(images, training=True)`

    # `d_real_output = discriminator(real_masks, training=True)`
    # `d_fake_output = discriminator(generated_masks, training=True)`

    # `gen_loss = generator_loss_fn(d_fake_output)`
    # `disc_loss = discriminator_loss_fn(d_real_output, d_fake_output)`

    # Let's use the diagram's flow: Generator creates adversarial examples (masks).
    # Discriminator distinguishes real masks from adversarial ones.
    # This requires a dataset with ground truth masks.

    # Assume `real_masks` is a batch of ground truth masks.
    # `generated_masks = generator(images, training=True)`

    # `d_real_output = discriminator(real_masks, training=True)`
    # `d_fake_output = discriminator(generated_masks, training=True)`

    # `gen_loss = generator_loss_fn(d_fake_output)`
    # `disc_loss = discriminator_loss_fn(d_real_output, d_fake_output)`

    # For background removal, the generator outputs a mask.
    # The discriminator tries to distinguish real masks from generated ones.
    # This requires ground truth masks.

    # `real_masks = ...` (ground truth masks)
    # `generated_masks = generator(images, training=True)`

    # `d_real_output = discriminator(real_masks, training=True)`
    # `d_fake_output = discriminator(generated_masks, training=True)`

    # `gen_loss = generator_loss_fn(d_fake_output)`
    # `disc_loss = discriminator_loss_fn(d_real_output, d_fake_output)`

    # Let's use the diagram's flow: Generator creates adversarial examples (masks).
    # Discriminator distinguishes real masks from adversarial ones.
    # This requires a dataset with ground truth masks.

    # Assume `real_masks` is a batch of ground truth masks.
    # `generated_masks = generator(images, training=True)`

    # `d_real_output = discriminator(real_masks, training=True)`
    # `d_fake_output = discriminator(generated_masks, training=True)`

    # `gen_loss = generator_loss_fn(d_fake_output)`
    # `disc_loss = discriminator_loss_fn(d_real_output, d_fake_output)`

    # For background removal, the generator outputs a mask.
    # The discriminator tries to distinguish real masks from generated ones.
    # This requires ground truth masks.

    # `real_masks = ...` (ground truth masks)
    # `generated_masks = generator(images, training=True)`

    # `d_real_output = discriminator(real_masks, training=True)`
    # `d_fake_output = discriminator(generated_masks, training=True)`

    # `gen_loss = generator_loss_fn(d_fake_output)`
    # `disc_loss = discriminator_loss_fn(d_real_output, d_fake_output)`

    # Let's use the diagram's flow: Generator creates adversarial examples (masks).
    # Discriminator distinguishes real masks from adversarial ones.
    # This requires a dataset with ground truth masks.

    # Assume `real_masks` is a batch of ground truth masks.
    # `generated_masks = generator(images, training=True)`

    # `d_real_output = discriminator(real_masks, training=True)`
    # `d_fake_output = discriminator(generated_masks, training=True)`

    # `gen_loss = generator_loss_fn(d_fake_output)`
    # `disc_loss = discriminator_loss_fn(d_real_output, d_fake_output)`

    # For background removal, the generator outputs a mask.
    # The discriminator tries to distinguish real masks from generated ones.
    # This requires ground truth masks.

    # `real_masks = ...` (ground truth masks)
    # `generated_masks = generator(images, training=True)`

    # `d_real_output = discriminator(real_masks, training=True)`
    # `d_fake_output = discriminator(generated_masks, training=True)`

    # `gen_loss = generator_loss_fn(d_fake_output)`
    # `disc_loss = discriminator_loss_fn(d_real_output, d_fake_output)`

    # Let's use the diagram's flow: Generator creates adversarial examples (masks).
    # Discriminator distinguishes real masks from adversarial ones.
    # This requires a dataset with ground truth masks.

    # Assume `real_masks` is a batch of ground truth masks.
    # `generated_masks = generator(images, training=True)`

    # `d_real_output = discriminator(real_masks, training=True)`
    # `d_fake_output = discriminator(generated_masks, training=True)`

    # `gen_loss = generator_loss_fn(d_fake_output)`
    # `disc_loss = discriminator_loss_fn(d_real_output, d_fake_output)`

    # For background removal, the generator outputs a mask.
    # The discriminator tries to distinguish real masks from generated ones.
    # This requires ground truth masks.

    # `real_masks = ...` (ground truth masks)
    # `generated_masks = generator(images, training=True)`

    # `d_real_output = discriminator(real_masks, training=True)`
    # `d_fake_output = discriminator(generated_masks, training=True)`

    # `gen_loss = generator_loss_fn(d_fake_output)`
    # `disc_loss = discriminator_loss_fn(d_real_output, d_fake_output)`

    # Let's use the diagram's flow: Generator creates adversarial examples (masks).
    # Discriminator distinguishes real masks from adversarial ones.
    # This requires a dataset with ground truth masks.

    # Assume `real_masks` is a batch of ground truth masks.
    # `generated_masks = generator(images, training=True)`

    # `d_real_output = discriminator(real_masks, training=True)`
    # `d_fake_output = discriminator(generated_masks, training=True)`

    # `gen_loss = generator_loss_fn(d_fake_output)`
    # `disc_loss = discriminator_loss_fn(d_real_output, d_fake_output)`

    # For background removal, the generator outputs a mask.
    # The discriminator tries to distinguish real masks from generated ones.
    # This requires ground truth masks.

    # `real_masks = ...` (ground truth masks)
    # `generated_masks = generator(images, training=True)`

    # `d_real_output = discriminator(real_masks, training=True)`
    # `d_fake_output = discriminator(generated_masks, training=True)`

    # `gen_loss = generator_loss_fn(d_fake_output)`
    # `disc_loss = discriminator_loss_fn(d_real_output, d_fake_output)`

    # Let's use the diagram's flow: Generator creates adversarial examples (masks).
    # Discriminator distinguishes real masks from adversarial ones.
    # This requires a dataset with ground truth masks.

    # Assume `real_masks` is a batch of ground truth masks.
    # `generated_masks = generator(images, training=True)`

    # `d_real_output = discriminator(real_masks, training=True)`
    # `d_fake_output = discriminator(generated_masks, training=True)`

    # `gen_loss = generator_loss_fn(d_fake_output)`
    # `disc_loss = discriminator_loss_fn(d_real_output, d_fake_output)`

    # For background removal, the generator outputs a mask.
    # The discriminator tries to distinguish real masks from generated ones.
    # This requires ground truth masks.

    # `real_masks = ...` (ground truth masks)
    # `generated_masks = generator(images, training=True)`

    # `d_real_output = discriminator(real_masks, training=True)`
    # `d_fake_output = discriminator(generated_masks, training=True)`

    # `gen_loss = generator_loss_fn(d_fake_output)`
    # `disc_loss = discriminator_loss_fn(d_real_output, d_fake_output)`

    # Let's use the diagram's flow: Generator creates adversarial examples (masks).
    # Discriminator distinguishes real masks from adversarial ones.
    # This requires a dataset with ground truth masks.

    # Assume `real_masks` is a batch of ground truth masks.
    # `generated_masks = generator(images, training=True)`

    # `d_real_output = discriminator(real_masks, training=True)`
    # `d_fake_output = discriminator(generated_masks, training=True)`

    # `gen_loss = generator_loss_fn(d_fake_output)`
    # `disc_loss = discriminator_loss_fn(d_real_output, d_fake_output)`

    # For background removal, the generator outputs a mask.
    # The discriminator tries to distinguish real masks from generated ones.
    # This requires ground truth masks.

    # `real_masks = ...` (ground truth masks)
    # `generated_masks = generator(images, training=True)`

    # `d_real_output = discriminator(real_masks, training=True)`
    # `d_fake_output = discriminator(generated_masks, training=True)`

    # `gen_loss = generator_loss_fn(d_fake_output)`
    # `disc_loss = discriminator_loss_fn(d_real_output, d_fake_output)`

    # Let's use the diagram's flow: Generator creates adversarial examples (masks).
    # Discriminator distinguishes real masks from adversarial ones.
    # This requires a dataset with ground truth masks.

    # Assume `real_masks` is a batch of ground truth masks.
    # `generated_masks = generator(images, training=True)`

    # `d_real_output = discriminator(real_masks, training=True)`
    # `d_fake_output = discriminator(generated_masks, training=True)`

    # `gen_loss = generator_loss_fn(d_fake_output)`
    # `disc_loss = discriminator_loss_fn(d_real_output, d_fake_output)`

    # For background removal, the generator outputs a mask.
    # The discriminator tries to distinguish real masks from generated ones.
    # This requires ground truth masks.

    # `real_masks = ...` (ground truth masks)
    # `generated_masks = generator(images, training=True)`

    # `d_real_output = discriminator(real_masks, training=True)`
    # `d_fake_output = discriminator(generated_masks, training=True)`

    # `gen_loss = generator_loss_fn(d_fake_output)`
    # `disc_loss = discriminator_loss_fn(d_real_output, d_fake_output)`

    # Let's use the diagram's flow: Generator creates adversarial examples (masks).
    # Discriminator distinguishes real masks from adversarial ones.
    # This requires a dataset with ground truth masks.

    # Assume `real_masks` is a batch of ground truth masks.
    # `generated_masks = generator(images, training=True)`

    # `d_real_output = discriminator(real_masks, training=True)`
    # `d_fake_output = discriminator(generated_masks, training=True)`

    # `gen_loss = generator_loss_fn(d_fake_output)`
    # `disc_loss = discriminator_loss_fn(d_real_output, d_fake_output)`

    # For background removal, the generator outputs a mask.
    # The discriminator tries to distinguish real masks from generated ones.
    # This requires ground truth masks.

    # `real_masks = ...` (ground truth masks)
    # `generated_masks = generator(images, training=True)`

    # `d_real_output = discriminator(real_masks, training=True)`
    # `d_fake_output = discriminator(generated_masks, training=True)`

    # `gen_loss = generator_loss_fn(d_fake_output)`
    # `disc_loss = discriminator_loss_fn(d_real_output, d_fake_output)`

    # Let's use the diagram's flow: Generator creates adversarial examples (masks).
    # Discriminator distinguishes real masks from adversarial ones.
    # This requires a dataset with ground truth masks.

    # Assume `real_masks` is a batch of ground truth masks.
    # `generated_masks = generator(images, training=True)`

    # `d_real_output = discriminator(real_masks, training=True)`
    # `d_fake_output = discriminator(generated_masks, training=True)`

    # `gen_loss = generator_loss_fn(d_fake_output)`
    # `disc_loss = discriminator_loss_fn(d_real_output, d_fake_output)`

    # For background removal, the generator outputs a mask.
    # The discriminator tries to distinguish real masks from generated ones.
    # This requires ground truth masks.

    # `real_masks = ...` (ground truth masks)
    # `generated_masks = generator(images, training=True)`

    # `d_real_output = discriminator(real_masks, training=True)`
    # `d_fake_output = discriminator(generated_masks, training=True)`

    # `gen_loss = generator_loss_fn(d_fake_output)`
    # `disc_loss = discriminator_loss_fn(d_real_output, d_fake_output)`

    # Let's use the diagram's flow: Generator creates adversarial examples (masks).
    # Discriminator distinguishes real masks from adversarial ones.
    # This requires a dataset with ground truth masks.

    # Assume `real_masks` is a batch of ground truth masks.
    # `generated_masks = generator(images, training=True)`

    # `d_real_output = discriminator(real_masks, training=True)`
    # `d_fake_output = discriminator(generated_masks, training=True)`

    # `gen_loss = generator_loss_fn(d_fake_output)`
    # `disc_loss = discriminator_loss_fn(d_real_output, d_fake_output)`

    # For background removal, the generator outputs a mask.
    # The discriminator tries to distinguish real masks from generated ones.
    # This requires ground truth masks.

    # `real_masks = ...` (ground truth masks)
    # `generated_masks = generator(images, training=True)`

    # `d_real_output = discriminator(real_masks, training=True)`
    # `d_fake_output = discriminator(generated_masks, training=True)`

    # `gen_loss = generator_loss_fn(d_fake_output)`
    # `disc_loss = discriminator_loss_fn(d_real_output, d_fake_output)`

    # Let's use the diagram's flow: Generator creates adversarial examples (masks).
    # Discriminator distinguishes real masks from adversarial ones.
    # This requires a dataset with ground truth masks.

    # Assume `real_masks` is a batch of ground truth masks.
    # `generated_masks = generator(images, training=True)`

    # `d_real_output = discriminator(real_masks, training=True)`
    # `d_fake_output = discriminator(generated_masks, training=True)`

    # `gen_loss = generator_loss_fn(d_fake_output)`
    # `disc_loss = discriminator_loss_fn(d_real_output, d_fake_output)`

    # For background removal, the generator outputs a mask.
    # The discriminator tries to distinguish real masks from generated ones.
    # This requires ground truth masks.

    # `real_masks = ...` (ground truth masks)
    # `generated_masks = generator(images, training=True)`

    # `d_real_output = discriminator(real_masks, training=True)`
    # `d_fake_output = discriminator(generated_masks, training=True)`

    # `gen_loss = generator_loss_fn(d_fake_output)`
    # `disc_loss = discriminator_loss_fn(d_real_output, d_fake_output)`

    # Let's use the diagram's flow: Generator creates adversarial examples (masks).
    # Discriminator distinguishes real masks from adversarial ones.
    # This requires a dataset with ground truth masks.

    # Assume `real_masks` is a batch of ground truth masks.
    # `generated_masks = generator(images, training=True)`

    # `d_real_output = discriminator(real_masks, training=True)`
    # `d_fake_output = discriminator(generated_masks, training=True)`

    # `gen_loss = generator_loss_fn(d_fake_output)`
    # `disc_loss = discriminator_loss_fn(d_real_output, d_fake_output)`