The idea behind the unsupervised learning of disentangled representations is that real-
world data is generated by a few explanatory factors of variation which can be recovered by …

The key idea behind the unsupervised learning of disentangled representations is that real-
world data is generated by a few explanatory factors of variation which can be recovered by …

We make two theoretical contributions to disentanglement learning by (a) defining precise
semantics of disentangled representations, and (b) establishing robust metrics for …

Learning disentangled representations is considered a cornerstone problem in
representation learning. Recently, Locatello et al.(2019) demonstrated that unsupervised …

A large part of the literature on learning disentangled representations focuses on variational
autoencoders (VAEs). Recent developments demonstrate that disentanglement cannot be …

We define and address the problem of unsupervised learning of disentangled
representations on data generated from independent factors of variation. We propose …

How can intelligent agents solve a diverse set of tasks in a data-efficient manner? The
disentangled representation learning approach posits that such an agent would benefit from …

Disentangled representations, where the higher level data generative factors are reflected in
disjoint latent dimensions, offer several benefits such as ease of deriving invariant …

Learning disentangled representations that correspond to factors of variation in real-world
data is critical to interpretable and human-controllable machine learning. Recently, concerns …

Learning to disentangle and represent factors of variation in data is an important problem in
artificial intelligence. While many advances have been made to learn these representations …