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 …

Continual learning aims to empower artificial intelligence with strong adaptability to the real
world. For this purpose, a desirable solution should properly balance memory stability with …

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

The goal of continual learning (CL) is to continuously learn new tasks without forgetting
previously learned old tasks. To alleviate catastrophic forgetting, gradient projection based …

Contrastive methods have led a recent surge in the performance of self-supervised
representation learning (SSL). Recent methods like BYOL or SimSiam purportedly distill …

Abstract Task-Free Continual Learning (TFCL) aims to learn new concepts from a stream of
data without any task information. The Dynamic Expansion Model (DEM) has shown …

It's widely acknowledged that deep learning models with flatter minima in its loss landscape
tend to generalize better. However, such property is under-explored in deep long-tailed …

Class-Incremental Learning (CIL) or continual learning is a desired capability in the real
world, which requires a learning system to adapt to new tasks without forgetting former ones …

Abstract Domain incremental learning aims to adapt to a sequence of domains with access
to only a small subset of data (ie, memory) from previous domains. Various methods have …