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 …

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 …

Learning meaningful representations that disentangle the underlying structure of the data
generating process is considered to be of key importance in machine learning. While …

Recent AI research has emphasised the importance of learning disentangled
representations of the explanatory factors behind data. Despite the growing interest in …

Disentangled Representation Learning (DRL) aims to learn a model capable of identifying
and disentangling the underlying factors hidden in the observable data in representation …

This paper presents a novel dimensionality reduction method for classification in medical
imaging. The goal is to transform very high-dimensional input (typically, millions of voxels) to …

Representation learning constructs low-dimensional representations to summarize essential
features of high-dimensional data. This learning problem is often approached by describing …

In this paper, we investigate the problem of learning disentangled representations. Given a
pair of images sharing some attributes, we aim to create a low-dimensional representation …

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 consider learning representations (features) in the setting in which we have access to
multiple unlabeled views of the data for representation learning while only one view is …