Reviews: Unsupervised Learning of Disentangled and Interpretable Representations from Sequential Data

This paper presents a factorized hierarchical variational autoencoder applied to unsupervised sequence modeling. The main claim of the paper is that the proposed model can disentangle the sequence representation into frame-level and sequence-level components. The sequence-level representation can be used for applications such as speaker verification, without any supervision in learning the representation, and they show that it is better than competitive unsupervised baselines such as using i-vector representations. The model is a mostly straightforward adaptation of sequential VAEs, with the addition of a discriminative regularizer that encourages sequence-level features to be able to predict sequence indices. Does this mean the actual index of a sequence in the training set?