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 …

Hypernetworks, or hypernets for short, are neural networks that generate weights for another
neural network, known as the target network. They have emerged as a powerful deep …

Incrementally learning new information from a non-stationary stream of data, referred to as
'continual learning', is a key feature of natural intelligence, but a challenging problem for …

In this article, we aim to provide a literature review of different formulations and approaches
to continual reinforcement learning (RL), also known as lifelong or non-stationary RL. We …

Continual learning (CL) learns a sequence of tasks incrementally. There are two popular CL
settings, class incremental learning (CIL) and task incremental learning (TIL). A major …

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 …

Deep ensembles have recently gained popularity in the deep learning community for their
conceptual simplicity and efficiency. However, maintaining functional diversity between …

This paper studies the challenging continual learning (CL) setting of Class Incremental
Learning (CIL). CIL learns a sequence of tasks consisting of disjoint sets of concepts or …

Abstract Online Task-Free Continual Learning (OTFCL) aims to learn novel concepts from
streaming data without accessing task information. Most memory-based approaches used in …

This paper studies class incremental learning (CIL) of continual learning (CL). Many
approaches have been proposed to deal with catastrophic forgetting (CF) in CIL. Most …