Image Generation Using Variational Autoencoders and Energy Based Models

Machine learning practitioners have long sought generative models that can accurately estimate the underlying data distribution and are able to produce diverse and semantically meaningful image samples. In this project, we take part in a similar quest. Our research directions include (but not limited to) developing improved variational inference techniques, coming up with more efficient methods to train energy based models and better traversing the latent space with Riemannian geometry and manifold assumption.