DC-GAN Implementation

This repository contains a PyTorch implementation of Deep Convolutional Generative Adversarial Network (DC-GAN) for image generation.

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Table of Contents

• Introduction
• Features
• Installation
• Usage
• Results
• References

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Introduction

DC-GAN is a type of Generative Adversarial Network that uses convolutional layers to generate realistic images. This implementation trains DC-GAN on datasets such as MNIST and CIFAR-10 to produce high-quality synthetic images.

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Features

• PyTorch-based implementation
• Supports training on CPU and GPU
• Saves model checkpoints and sample generated images
• Configurable hyperparameters (learning rate, epochs, batch size)
• Visualization of training losses and generated samples

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Installation

git clone https://github.com/your-username/dc-gan.git
cd dc-gan
python -m venv venv

Activate Conva Env

source venv/bin/activate  # Linux/

Or

venv\Scripts\activate     # Windows

Install Requirements

pip install -r requirements.txt

Usage

Train The Model

python train.py

Evaluate FID Score

Evaluate FID Score of a Single ckpt

python eval.py --ckpt {ckpt_path} --csv {fid_scores_csv_path}

Evaluate FID Scores across all ckpts

python eval_all_ckpts.py

Results

Example generated images after training:

Training loss curves are saved during training for visualization.

References

• Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014).
  Generative Adversarial Nets.
  https://arxiv.org/abs/1406.2661

• Radford, A., Metz, L., & Chintala, S. (2015).
  Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks.
  https://arxiv.org/abs/1511.06434