We propose an approach to learn image representations that consist of disentangled factors
of variation without exploiting any manual labeling or data domain knowledge. A factor of …

Contrastive learning has become an important tool in learning representations from
unlabeled data mainly relying on the idea of minimizing distance between positive data …

Deep neural networks require a large amount of labeled training data during supervised
learning. However, collecting and labeling so much data might be infeasible in many cases …

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 …

Self-supervised learning algorithms based on instance discrimination train encoders to be
invariant to pre-defined transformations of the same instance. While most methods treat …

We propose an algorithm, guided variational autoencoder (Guided-VAE), that is able to
learn a controllable generative model by performing latent representation disentanglement …

One desired capability for machines is the ability to transfer their understanding of one
domain to another domain where data is (usually) scarce. Despite ample adaptation of …

We address the problem of disentanglement of factors that generate a given data into those
that are correlated with the labeling and those that are not. Our solution is simpler than …

We propose a general framework called Network Dissection for quantifying the
interpretability of latent representations of CNNs by evaluating the alignment between …

Disentangling factors of variation has become a very challenging problem on representation
learning. Existing algorithms suffer from many limitations, such as unpredictable …