With the widespread use of artificial intelligence (AI) systems and applications in our
everyday lives, accounting for fairness has gained significant importance in designing and …

In this paper, we explore a novel and ambitious knowledge-transfer task, termed Knowledge
Factorization (KF). The core idea of KF lies in the modularization and assemblability of …

The clustering methods have recently absorbed even-increasing attention in learning and
vision. Deep clustering combines embedding and clustering together to obtain optimal …

Data augmentation is often used to enlarge datasets with synthetic samples generated in
accordance with the underlying data distribution. To enable a wider range of augmentations …

The information bottleneck (IB) method is a technique for extracting information that is
relevant for predicting the target random variable from the source random variable, which is …

In recent years, clustering methods based on deep generative models have received great
attention in various unsupervised applications, due to their capabilities for learning …

Object detection models perform well at localizing and classifying objects that they are
shown during training. However, due to the difficulty and cost associated with creating and …

We propose a novel approach to achieving invariance for deep neural networks in the form
of inducing amnesia to unwanted factors of data through a new adversarial forgetting …

Nowadays, deep clustering achieves superior performance by jointly performing
representation learning and cluster assignment. Although numerous deep clustering …

We propose to learn invariant representations, in the data domain, to achieve interpretability
in algorithmic fairness. Invariance implies a selectivity for high level, relevant correlations wrt …