Humans and animals have the ability to continually acquire, fine-tune, and transfer
knowledge and skills throughout their lifespan. This ability, referred to as lifelong learning, is …

In this article, we challenge the usefulness of “attention” as a unitary construct and/or neural
system. We point out that the concept has too many meanings to justify a single term, and …

Class-incremental learning (CIL) aims to adapt to emerging new classes without forgetting
old ones. Traditional CIL models are trained from scratch to continually acquire knowledge …

Class-incremental learning (CIL) aims to adapt to emerging new classes without forgetting
old ones. Traditional CIL models are trained from scratch to continually acquire knowledge …

Deep models, eg, CNNs and Vision Transformers, have achieved impressive achievements
in many vision tasks in the closed world. However, novel classes emerge from time to time in …

Artificial neural networks thrive in solving the classification problem for a particular rigid task,
acquiring knowledge through generalized learning behaviour from a distinct training phase …

Continual learning, the setting where a learning agent is faced with a never-ending stream
of data, continues to be a great challenge for modern machine learning systems. In …

Real-world applications require the classification model to adapt to new classes without
forgetting old ones. Correspondingly, Class-Incremental Learning (CIL) aims to train a …

Attaining prototypical features to represent class distributions is well established in
representation learning. However, learning prototypes online from streaming data proves a …

This article proposes that biologically plausible theories of behavior can be constructed by
following a method of “phylogenetic refinement,” whereby they are progressively elaborated …