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) incrementally learns a sequence of tasks while solving the
catastrophic forgetting (CF) problem. Existing methods mainly try to deal with CF directly. In …

Continual learning (CL) learns a sequence of tasks incrementally with the goal of achieving
two main objectives: overcoming catastrophic forgetting (CF) and encouraging knowledge …

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 …

Existing literature in Continual Learning (CL) has focused on overcoming catastrophic
forgetting, the inability of the learner to recall how to perform tasks observed in the past …

Existing research on continual learning of a sequence of tasks focused on dealing with
catastrophic forgetting, where the tasks are assumed to be dissimilar and have little shared …

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

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 …

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 …

When optimizing sequentially incoming tasks, deep neural networks generally suffer from
catastrophic forgetting due to their lack of ability to maintain knowledge from old tasks. This …