GAN-FashionMNIST

Generative Adversarial Networks (GAN) – Fashion MNIST

This project implements Generative Adversarial Networks (GANs) for image generation using the Fashion-MNIST dataset. We compare two architectures:

• Vanilla GAN (Fully Connected Networks)
• Deep Convolutional GAN (DCGAN)

The goal is to analyze:

• Image quality
• Training stability
• Effect of architecture on performance

Dataset

• Fashion-MNIST
• 28×28 grayscale images of clothing items
• Dataset automatically downloaded using PyTorch

Preprocessing

• Converted images to tensors
• Normalized pixel values to range [-1, 1]

Model Architectures

1. Vanilla GAN

• Generator

  • Fully connected layers
  • ReLU activations

• Discriminator

  • Fully connected layers
  • LeakyReLU activations

2. DCGAN

• Generator

  • Transposed Convolution layers
  • Batch Normalization
  • ReLU activations

• Discriminator

  • Convolution layers
  • Batch Normalization
  • LeakyReLU activations

Training Process

1. Train discriminator on:

   • Real images (label = 1)
   • Fake images (label = 0)

2. Train generator to:

   • Fool discriminator (label = 1)

3. Loss function:

   • Binary Cross Entropy (BCE)

4. Optimizer:

   • Adam optimizer (lr = 0.0002)

Observations

• DCGAN produces sharper and more realistic images
• Vanilla GAN generates blurry outputs
• Training is unstable in early epochs

Loss Behavior

• Generator and discriminator losses oscillate
• DCGAN shows relatively stable convergence