Deep Convolutional Generative Adversarial Network (DCGAN) for Image Generation

Overview

This repository contains an implementation of a Deep Convolutional Generative Adversarial Network (DCGAN) for generating high-quality images. The model is trained using deep learning techniques to generate realistic images by learning the underlying data distribution.

Dataset

The dataset consists of a collection of images used to train the DCGAN. These images undergo preprocessing to enhance training efficiency.

Preprocessing

• Images are loaded using PIL and torchvision.transforms.
• Resized to a fixed dimension suitable for the model.
• Normalized to a range of [-1,1] for better convergence.
• Converted into PyTorch tensors for GPU acceleration.

Model Architecture

DCGAN consists of:

• Generator: A deep convolutional neural network that generates realistic images from random noise.
• Discriminator: A convolutional classifier that distinguishes between real and generated images.
• Loss Function: Uses Binary Cross-Entropy (BCE) loss to optimize both networks.

The architecture follows best practices for training GANs using deep convolutional layers, batch normalization, and LeakyReLU activations.

Training

• The model is trained using adversarial training, where the Generator and Discriminator compete to improve generation quality.
• Uses the Adam optimizer with tuned hyperparameters.
• Trained for multiple epochs with loss values monitored for both networks.
• Includes visualizations to track the progress of generated images over time.

Evaluation

• Generated images are visually inspected for realism.
• Loss curves are plotted to evaluate training stability.
• The quality of generated images improves progressively with training.

Results

• The model successfully generates high-quality images.
• Loss curves indicate stable adversarial training dynamics.
• The Generator learns to produce more detailed and realistic images over time.