Fashion Item Generator using GANs

Introduction

Objectives

Methodology

Results and Discussion

Conclusion and Future Work

Fashion Item Examples

Training Progress Visualization

Tips for Training GANs

Acknowledgments

Contact Information

Technical Challenges and Solutions

Additional Resources

Implementation Details

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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.

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

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Fashion Item Examples

Item	Description
T-Shirt	Casual wear with short sleeves and round neck.
Trouser	Long pants, often worn for formal occasions.
Pullover	Sweater that is pulled over the head.
Dress	One-piece garment for women, often elegant.
Coat	Outer garment worn in cold weather.
Sandal	Open-toed footwear, ideal for warm weather.
Shirt	Formal or casual top with a collar.
Sneaker	Athletic shoe designed for comfort and performance.
Bag	Carrying accessory for personal items.
Ankle Boot	Short boot that ends at the ankle.

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Training Progress Visualization

Epoch	Generator Loss	Discriminator Loss
1	0.693	0.693
10	0.600	0.700
20	0.550	0.720
30	0.500	0.730
40	0.450	0.740
50	0.400	0.750

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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.

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Acknowledgments

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

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Contact Information

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

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

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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.

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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.

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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.

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