Numerous deep learning algorithms have been inspired by and understood via the notion of
information bottleneck, where unnecessary information is (often implicitly) minimized while …

We present a new dataset condensation framework termed Squeeze, Recover and Relabel
(SRe $^ 2$ L) that decouples the bilevel optimization of model and synthetic data during …

Most deep learning models face difficulties in analysing image information due to the
concept of information bottlenecks and their corresponding methodologies. But, the …

Multiview learning has drawn widespread attention for its efficacy in leveraging cross-view
consensus and complementarity information to achieve a comprehensive representation of …

Abstract Deep Neural Networks (DNNs) are a modeling technique capable of automatically
extracting complex patterns from data, key to the modern practice of Deep Learning (DL). It …

The failure of deep neural networks to generalize to out-of-distribution (OOD) data is a well-
known problem that raises concerns about the deployment of trained networks in safety …