Conditional Generation with Variational Autoencoders and Generative Adversarial Networks Overview

In this project, you will work with Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs). You will build and train conditional versions of these models to generate MNIST digits given a digit class (see also Labels improve subjective sample quality, in week 12).

Objectives

1. Conditional Variational Autoencoder (C-VAE): Implement a Conditional VAE that takes an MNIST image and its associated label as input. The model should learn to generate new images that resemble the input image given a class label.
2. Conditional Generative Adversarial Network (C-GAN): Implement a Conditional GAN that takes a random noise vector and a class label as input to the generator, and produces a digit of the specified class. The discriminator should also be conditioned on the class label.
3. Model Comparison: Compare the performance of the two models. Discuss their strengths and weaknesses, and compare the quality of the generated samples. Use both qualitative (visual) and quantitative measures (if possible) for this comparison.
4. Extra Challenge (Optional): Experiment with different architectures, training strate- gies, and techniques for improving the quality and diversity of the generated images (such as different types of regularisation, different architectures, etc.). Document your findings and provide explanations for the observed results.