In the recent years, lifelong learning (LL) has attracted a great deal of attention in the deep
learning community, where it is often called continual learning. Though it is well-known that …

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) has received a great amount of attention in recent years and a
multitude of continual learning approaches arose. In this paper, we propose a continual …

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 …

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 …

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 …

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 enables learning systems to adapt to evolving data distributions by
sequentially acquiring knowledge from a series of tasks. Unsupervised lifelong learning …

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" …

Supervised Continual learning involves updating a deep neural network (DNN) from an ever-
growing stream of labeled data. While most work has focused on overcoming catastrophic …