GAN vs VAE: Custom Dataset Image Generation

This repository contains a project where Generative Adversarial Networks (GAN) and Variational Autoencoders (VAE) are trained on a custom dataset to generate images. The results from the two models are then compared to evaluate their performance in terms of image generation quality.

Overview

Objective:

• Train GAN and VAE models on a custom image dataset.
• Generate new images using both models.
• Compare the generated images to analyze the differences and strengths of each model.

Models Implemented:

• GAN (Generative Adversarial Network): A model composed of a Generator and a Discriminator, where the generator tries to create realistic images and the discriminator attempts to distinguish between real and generated images.
• VAE (Variational Autoencoder): A type of autoencoder that learns to generate images by mapping input data into a latent space and reconstructing from that space.

Results

Criteria	GAN	VAE
Image Sharpness	High sharpness, realistic details	Lower sharpness, often blurry
Training Stability	Unstable, requires careful tuning	Stable and predictable
Latent Space	Less structured, less interpretable	Well-organized, smooth interpolation
Image Diversity	Risk of mode collapse, limited diversity	High diversity, good variation
Training Time	Longer, due to unstable convergence	Faster and stable