Reviews: Life-Long Disentangled Representation Learning with Cross-Domain Latent Homologies

The authors present an algorithm for lifelong representation learning that adapts variational autoencoders to the lifelong learning setting. The framework is presented as a full generative process, where a set of latent factors are (selectively) shared across tasks, and the tasks themselves are generated by an unknown distribution. The algorithm optimizes for the reconstruction error with a regularization based on the MDL principle that has been studied for learning disentangled representations. The algorithm automatically detects distribution shifts (i.e., task changes) and avoids catastrophic forgetting by "hallucinating" data for previous tasks while training on a new one. The authors show empirically that their algorithm is able to extract relevant semantic knowledge from one task and transfer it to the next.