Existing continual learning (CL) research regards catastrophic forgetting (CF) as almost the
only challenge. This paper argues for another challenge in class-incremental learning (CIL) …

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 …

Continual learning (CL) aims to develop techniques by which a single model adapts to an
increasing number of tasks encountered sequentially, thereby potentially leveraging …

Online class-incremental continual learning is a specific task of continual learning. It aims to
continuously learn new classes from data stream and the samples of data stream are seen …

Existing continual learning techniques focus on either task incremental learning (TIL) or
class incremental learning (CIL) problem, but not both. CIL and TIL differ mainly in that the …

Catastrophic forgetting is one of the most critical challenges in Continual Learning (CL).
Recent approaches tackle this problem by projecting the gradient update orthogonal to the …

Catastrophic forgetting has been a major challenge in continual learning, where the model
needs to learn new tasks with limited or no access to data from previously seen tasks. To …

Many works have tried to solve the catastrophic forgetting (CF) problem in continual learning
(lifelong learning). However, pursuing non-forgetting on old tasks may damage the model's …

In most machine learning algorithms, training data is assumed to be independent and
identically distributed (iid). When it is not the case, the algorithm's performances are …

Task-incremental continual learning refers to continually training a model in a sequence of
tasks while overcoming the problem of catastrophic forgetting (CF). The issue arrives for the …