Unsupervised disentanglement learning is a crucial issue for understanding and exploiting
deep generative models. Recently, SeFa tries to find latent disentangled directions by …

Optimization is a ubiquitous modeling tool and is often deployed in settings which
repeatedly solve similar instances of the same problem. Amortized optimization methods …

The problem of catastrophic forgetting occurs in deep learning models trained on multiple
databases in a sequential manner. Recently, generative replay mechanisms (GRM) have …

Abstract Multi-View Representation Learning (MVRL) aims to discover a shared
representation of observations from different views with the complex underlying correlation …

A prominent goal of representation learning research is to achieve representations which
are factorized in a useful manner with respect to the ground truth factors of variation. The …

Disentangled generative models map a latent code vector to a target space, while enforcing
that a subset of the learned latent codes are interpretable and associated with distinct …

Cross-domain disentanglement is the problem of learning representations partitioned into
domain-invariant and domain-specific representations, which is a key to successful domain …

Recent research has shown that generative models with highly disentangled
representations fail to generalise to unseen combination of generative factor values. These …

Capturing interpretable variations has long been one of the goals in disentanglement
learning. However, unlike the independence assumption, interpretability has rarely been …

Learning disentangled and interpretable representations is an important aspect of
information understanding. In this paper, we propose a novel deep learning model …