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 …

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 …

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 …

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 …

The goal of continual learning is to find a model that solves multiple learning tasks which are
presented sequentially to the learner. A key challenge in this setting is that the learner may" …

A growing body of research in continual learning focuses on the catastrophic forgetting
problem. While many attempts have been made to alleviate this problem, the majority of the …

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 …

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 …

Approaches to continual learning aim to successfully learn a set of related tasks that arrive in
an online manner. Recently, several frameworks have been developed which enable deep …

While recent continual learning methods largely alleviate the catastrophic problem on toy-
sized datasets, some issues remain to be tackled to apply them to real-world problem …