By learning a sequence of tasks continually, an agent in continual learning (CL) can improve
the learning performance of both a new task andold'tasks by leveraging the forward …

Continual learning (CL) of a sequence of tasks is often accompanied with the catastrophic
forgetting (CF) problem. Existing research has achieved remarkable results in overcoming …

Continual learning (CL) has two main objectives: preventing catastrophic forgetting (CF) and
encouraging knowledge transfer (KT). The existing literature mainly focused on overcoming …

Continual learning aims to provide intelligent agents that are capable of learning continually
a sequence of tasks, building on previously learned knowledge. A key challenge in this …

Existing research on task incremental learning in continual learning has primarily focused
on preventing catastrophic forgetting (CF). Although several techniques have achieved …

In continual learning (CL), an agent learns from a stream of tasks leveraging prior
experience to transfer knowledge to future tasks. It is an ideal framework to decrease the …

Distribution shift (eg, task or domain shift) in continual learning (CL) usually results in
catastrophic forgetting of previously learned knowledge. Although it can be alleviated by …

Continual learning aims to provide intelligent agents capable of learning multiple tasks
sequentially with neural networks. One of its main challenging, catastrophic forgetting, is …

In contrast to the natural capabilities of humans to learn new tasks in a sequential fashion,
neural networks are known to suffer from catastrophic forgetting, where the model's …

The ability of intelligent agents to learn and remember multiple tasks sequentially is crucial
to achieving artificial general intelligence. Many continual learning (CL) methods have been …