Improving Sampling from Generative Autoencoders with Markov Chains

We focus on generative autoencoders, such as variational or adversarial autoencoders, which jointly learn a generative model alongside an inference model. We define generative autoencoders as autoencoders which are trained to softly enforce a prior on the latent distribution learned by the model. However, the model does not necessarily learn to match the prior. We formulate a Markov chain Monte Carlo (MCMC) sampling process, equivalent to iteratively encoding and decoding, which allows us to sample from the learned latent distribution. Using this we can improve the quality of samples drawn from the model, especially when the learned distribution is far from the prior. Using MCMC sampling, we also reveal previously unseen differences between generative autoencoders trained either with or without the denoising criterion

Learning Disentangled Representations with Reference-Based Variational Autoencoders

Learning disentangled representations from visual data, where different high-level generative factors are independently encoded, is of importance for many computer vision tasks. Solving this problem, however, typically requires to explicitly label all the factors of interest in training images. To alleviate the annotation cost, we introduce a learning setting which we refer to as "reference-based disentangling". Given a pool of unlabeled images, the goal is to learn a representation where a set of target factors are disentangled from others. The only supervision comes from an auxiliary "reference set" containing images where the factors of interest are constant. In order to address this problem, we propose reference-based variational autoencoders, a novel deep generative model designed to exploit the weak-supervision provided by the reference set. By addressing tasks such as feature learning, conditional image generation or attribute transfer, we validate the ability of the proposed model to learn disentangled representations from this minimal form of supervision