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 …

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

A good visual representation is an inference map from observations (images) to features
(vectors) that faithfully reflects the hidden modularized generative factors (semantics). In this …

There is growing interest in concept-based models (CBMs) that combine high-performance
and interpretability by acquiring and reasoning with a vocabulary of high-level concepts. A …

In disentangled representation learning, a model is asked to tease apart a dataset's
underlying sources of variation and represent them independently of one another. Since the …

In recent years, the rapid development of deep learning approaches has paved the way to
explore the underlying factors that explain the data. In particular, several methods have …

Recent work on interpretability has focused on concept-based explanations, where deep
learning models are explained in terms of high-level units of information, referred to as …

Deep generative models such as Variational Autoencoders (VAEs), Generative Adversarial
Networks (GANs), Diffusion Models, and Transformers, have shown great promise in a …

Nonlinear independent component analysis (ICA) aims to uncover the true latent sources
from their observable nonlinear mixtures. Despite its significance, the identifiability of …

Learning visual concepts from raw images without strong supervision is a challenging task.
In this work, we show the advantages of prototype representations for understanding and …