Disentangled representation learning has been proposed as an approach to learning general representations even in the absence of, or with limited, supervision. A good general …
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 …
Image-to-image translation aims to learn the mapping between two visual domains. There are two main challenges for many applications: 1) the lack of aligned training pairs and 2) …
We decompose the evidence lower bound to show the existence of a term measuring the total correlation between latent variables. We use this to motivate the beta-TCVAE (Total …
We present new intuitions and theoretical assessments of the emergence of disentangled representation in variational autoencoders. Taking a rate-distortion theory perspective, we …
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 …
Intelligent agents should be able to learn useful representations by observing changes in their environment. We model such observations as pairs of non-iid images sharing at least …
For an autonomous agent to fulfill a wide range of user-specified goals at test time, it must be able to learn broadly applicable and general-purpose skill repertoires. Furthermore, to …
Learning an interpretable factorised representation of the independent data generative factors of the world without supervision is an important precursor for the development of …