Gen AI using GAN's

Introduction

Generative Adversarial Networks (GANs) are a class of deep learning models designed for generating synthetic data. This project focuses on developing a GAN to generate realistic fashion items inspired by the Fashion MNIST dataset, which consists of grayscale images of various clothing items such as shirts, shoes, and dresses.

Objectives

• Develop a GAN model capable of generating realistic fashion images.
• Train the generator and discriminator using the Fashion MNIST dataset.
• Evaluate the model’s performance in generating high-quality images.
• Optimize the training process using appropriate loss functions and optimizers.

Methodology

Dataset

• Source: Fashion MNIST
• Content: 60,000 training images and 10,000 test images.
• Preprocessing:
  • Normalization: Pixel values are normalized to the range [-1, 1] to match the Tanh activation function used in the generator’s output layer.
  • Resizing: The images remain at their original size of 28x28 pixels.
  • Channel Dimension: Added a channel dimension to accommodate grayscale images, resulting in a shape of (28, 28, 1).

Model Architecture

Generator Model

• Purpose: Transform a random noise vector into a meaningful image.
• Input: 100-dimensional latent vector.
• Layers:
  • Fully connected layer to reshape input noise.
  • Three transposed convolution layers with ReLU activation:
    • Filters: 64, 128, 256
  • Final convolution layer with Tanh activation to generate the output image.
  • Batch Normalization: Not used in this implementation.

Discriminator Model

• Purpose: Distinguish between real and fake images.
• Layers:
  • Fully connected layer to flatten input images.
  • Two dense layers with LeakyReLU activation and dropout:
    • Units: 256, 128
  • Final output layer with sigmoid activation for binary classification.
  • Batch Normalization: Not used in this implementation.

Training Process

• Generator: Takes a random noise vector and generates an image.
• Discriminator: Classifies real images (from the dataset) and fake images (from the generator).
• Loss Functions: Binary Cross-Entropy Loss for both models.
• Optimizer: Adam optimizer with a learning rate of 1e-4.
  • Reason for Adam: Chosen for its adaptive learning rate capabilities, which are beneficial for training GANs where the loss landscape can be complex and challenging.
• Epochs: 50
• Batch Size: 128
• Gradient Clipping/Regularization: Not applied in this implementation.
• Data Augmentation: No augmentation techniques were used.

Training Implementation

• Visualization: Periodic visualization of generated images to track performance.
• Checkpoints: Saved periodically to allow for model restoration.

Results and Discussion

• Generator Improvement: Significant improvement over epochs, producing visually realistic fashion items.
• Discriminator Performance: Effectively distinguished between real and fake images, stabilizing after initial fluctuations.
• Loss Trends: Convergence indicated a balanced GAN training.
• Generated Images: Showed clear details and patterns resembling real fashion items.

Conclusion and Future Work

• Achievements: Successfully learned patterns from the dataset and produced visually appealing results.
• Future Improvements:
  • Train on higher-resolution datasets.
  • Use advanced GAN architectures like DCGAN or StyleGAN.
  • Fine-tune hyperparameters to enhance image diversity and realism.

Project Completed by: [Sanjan B M] Date: [05-03-2025]

Fashion Item Examples

Training Progress Visualization

Tips for Training GANs

1. Start Simple: Begin with a basic architecture and gradually add complexity.

2. Monitor Loss: Keep an eye on both generator and discriminator losses to ensure balanced training.

3. Visualize Outputs: Regularly check the generated images to assess quality improvements.

4. Experiment with Hyperparameters: Adjust learning rates, batch sizes, and other parameters to optimize performance.

5. Use Normalization: Normalize inputs to stabilize training and improve convergence.

Acknowledgments

• Dataset: Fashion MNIST
• Framework: TensorFlow/Keras
• Inspiration: Various online tutorials and research papers on GANs

Contact Information

For any inquiries or collaborations, feel free to reach out:

• Email: sanjanacharaya1234@gmail.com
• GitHub: sanjanb
• LinkedIn: Sanjan BM

Technical Challenges and Solutions

• Mode Collapse: Implement techniques like mini-batch discrimination or use advanced GAN architectures.
• Training Instability: Use techniques like gradient penalty or two-time-scale update rule (TTUR) to stabilize training.
• Evaluation Metrics: Use metrics like Inception Score (IS) or Fréchet Inception Distance (FID) to evaluate the quality and diversity of generated images.

Additional Resources

• Research Papers:
  • “Generative Adversarial Nets” by Goodfellow et al.
  • “Improved Techniques for Training GANs” by Salimans et al.
• Online Courses:
  • Deep Learning Specialization by Andrew Ng on Coursera.
  • Fast.ai Practical Deep Learning for Coders.
• Communities:
  • Kaggle: Participate in GAN-related competitions.
  • Reddit: r/MachineLearning for discussions and latest trends.

Implementation Details

• Data Preparation:
  • Loaded and normalized the Fashion MNIST dataset to the range [-1, 1].
  • Reshaped the dataset to include a channel dimension for grayscale images.
• Model Training:
  • Defined generator and discriminator models using the Keras Sequential API.
  • Trained using binary cross-entropy loss and the Adam optimizer.
• Training Loop:
  • Generated images from random noise and updated the discriminator and generator models.
  • Visualized training progress by generating and plotting images at regular intervals.
  • Saved checkpoints periodically for model restoration.
• Performance Monitoring:
  • Tracked loss values and the discriminator’s ability to distinguish between real and fake images.
  • Evaluated the generator’s performance based on the quality and diversity of the generated images.
• Technique:
  • This project utilizes a GAN architecture with dense neural networks to generate fashion items.

Note: This project is for educational purposes and aims to demonstrate the capabilities of GANs in generating fashion items.

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