Reviews: Learning Deep Disentangled Embeddings With the F-Statistic Loss

This approach tries to connect together the literature of learning deep embeddings and the literature on disentangled representation learning. In particular, it leverages the weakly supervised training process of the deep embeddings literature, which relies on the availability of data that either belongs to a class (shares a particular generative factor) or not. The authors encourage disentangling by separating the examples of the different classes based on independent per-embedding-dimension hypothesis testing using the F-statistic loss. Two examples are considered to belong to different classes based on their dissimilarity on a subset of d dimensions of the embedding, which allows the approach to be more flexible. The authors also show that this approach works when the separation is done not on class labels, but on attribute features.