Generative Models: VAEs, GANs, and Diffusion

Implement a Variational Autoencoder (VAE) using the reparameterization trick

Derive the Evidence Lower Bound (ELBO) objective for VAEs

Build a basic Generative Adversarial Network (GAN) and diagnose common training issues like mode collapse

Implement a Deep Convolutional GAN (DCGAN)

Implement Wasserstein GANs (WGANs) for improved training stability

Implement advanced GAN architectures for controlled and high-fidelity generation (Conditional GANs, StyleGAN)

Implement the forward and reverse processes of Denoising Diffusion Probabilistic Models (DDPM)

Implement a Latent Diffusion Model (LDM) like the one used in Stable Diffusion

Apply classifier-free guidance for conditional image generation