UCLA

Learning good representations without supervision remains a key challenge in machine learn- ing. We proposed to learn an energy-based model (EBM) in the latent space that stands on the deep generative model. Different from the original latent vector space, we formulate a convolutional feature map EBM in the prior. Using short-run MCMC sampling from both the prior and posterior distributions of the latent vector, both the prior EBM and the generator model are learned jointly. Using the convolutional EBM method allows the model to exhibit strong performances in terms of image generation and capture more explainable representations.