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 (CL) aims to sequentially train models on streams of incoming data that
vary in distribution by preserving previous knowledge while adapting to new data. Current …

Abstract Current evaluations of Continual Learning (CL) methods typically assume that there
is no constraint on training time and computation. This is an unrealistic assumption for any …

Learning from non-stationary data streams, also called Task-Free Continual Learning
(TFCL) remains challenging due to the absence of explicit task information in most …

The goal of continual learning is to find a model that solves multiple learning tasks which are
presented sequentially to the learner. A key challenge in this setting is that the learner may" …

Traditional online continual learning (OCL) research has primarily focused on mitigating
catastrophic forgetting with fixed and limited storage allocation throughout an agent's …

Artificial Intelligence (AI) has achieved significant advancements in technology and research
with the development over several decades, and is widely used in many areas including …

Multi-task learning ideally allows embodied agents such as robots to acquire a diverse
repertoire of useful skills. However, many multi-task reinforcement learning efforts assume …

Current class-incremental learning research mainly focuses on single-label classification
tasks while multi-label class-incremental learning (MLCIL) with more practical application …