Reviews: Continual Unsupervised Representation Learning

After rebuttal I thank the authors for their response, they have managed to clarify some of my concerns and overall I vote for acceptance of the paper. The authors introduce a method for continual unsupervised learning. They propose a generative categorical model, in which the latent space is modeled as a mixture of Gaussians, with a Bernoulli decoder. An expansion technique is used to include new mixture components for poorly modeled examples, and the generative model is used with previous model parameters to prevent forgetting old tasks. Their method is analysed on tasks constructed around MNIST and Omniglot, with an ablation study on the expansion and generative replay.