Continual learning is a setting where machine learning models learn novel concepts from
continuously shifting training data, while simultaneously avoiding degradation of knowledge …

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 …

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 …

The ability to continuously learn and adapt itself to new tasks, without losing grasp of already
acquired knowledge is a hallmark of biological learning systems, which current deep …

Continual learning is an emerging research challenge in machine learning that addresses
the problem where models quickly fit the most recently trained-on data but suffer from …

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 …

A major obstacle to developing artificial intelligence applications capable of true lifelong
learning is that artificial neural networks quickly or catastrophically forget previously learned …

In Continual learning (CL) balancing effective adaptation while combating catastrophic
forgetting is a central challenge. Many of the recent best-performing methods utilize various …

Catastrophic forgetting is one of the major challenges on the road for continual learning
systems, which are presented with an on-line stream of tasks. The field has attracted …

We introduce a new training paradigm that enforces interval constraints on neural network
parameter space to control forgetting. Contemporary Continual Learning (CL) methods focus …