To cope with real-world dynamics, an intelligent system needs to incrementally acquire,
update, accumulate, and exploit knowledge throughout its lifetime. This ability, known as …

Forgetting refers to the loss or deterioration of previously acquired knowledge. While
existing surveys on forgetting have primarily focused on continual learning, forgetting is a …

Recent breakthroughs in self-supervised learning show that such algorithms learn visual
representations that can be transferred better to unseen tasks than cross-entropy based …

Online continual learning for image classification studies the problem of learning to classify
images from an online stream of data and tasks, where tasks may include new classes …

We propose an algorithm that compresses the critical information of a large dataset into
compact addressable memories. These memories can then be recalled to quickly re-train a …

Abstract Inspired by Lottery Ticket Hypothesis that competitive subnetworks exist within a
dense network, we propose a continual learning method referred to as Winning …

Compared to traditional machine learning, deep learning can learn deeper abstract data
representation and understand scattered data properties. It has gained considerable …

The lifelong learning paradigm in machine learning is an attractive alternative to the more
prominent isolated learning scheme not only due to its resemblance to biological learning …

Continual learning (CL) is a learning paradigm that emulates the human capability of
learning and accumulating knowledge continually without forgetting the previously learned …

Meta-learning empowers learning systems with the ability to acquire knowledge from
multiple tasks, enabling faster adaptation and generalization to new tasks. This review …