Continual learning (CL), which aims to learn a sequence of tasks, has attracted significant
recent attention. However, most work has focused on the experimental performance of CL …

We study the relationship between catastrophic forgetting and properties of task sequences.
In particular, given a sequence of tasks, we would like to understand which properties of this …

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 …

Continual Learning (CL) research typically focuses on tackling the phenomenon of
catastrophic forgetting in neural networks. Catastrophic forgetting is associated with an …

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 …

A large body of research in continual learning is devoted to overcoming the catastrophic
forgetting of neural networks by designing new algorithms that are robust to the distribution …

Despite the growing interest in continual learning, most of its contemporary works have been
studied in a rather restricted setting where tasks are clearly distinguishable, and task …

Continual learning (CL) is a major challenge of machine learning (ML) and describes the
ability to learn several tasks sequentially without catastrophic forgetting (CF). Recent works …

Standard artificial neural networks suffer from the well-known issue of catastrophic
forgetting, making continual or lifelong learning difficult for machine learning. In recent years …

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 …