To cope with real-world dynamics, an intelligent system needs to incrementally acquire,
update, accumulate, and exploit knowledge throughout its lifetime. This ability, known as …

Forgetting refers to the loss or deterioration of previously acquired knowledge. While
existing surveys on forgetting have primarily focused on continual learning, forgetting is a …

Transfer learning (TL) utilizes data or knowledge from one or more source domains to
facilitate learning in a target domain. It is particularly useful when the target domain has very …

A primary goal of class-incremental learning is to strike a balance between stability and
plasticity, where models should be both stable enough to retain knowledge learned from …

A primary focus area in continual learning research is alleviating the" catastrophic forgetting"
problem in neural networks by designing new algorithms that are more robust to the …

Continual learning {—} the ability to learn many tasks in sequence {—} is critical for artificial
learning systems. Yet standard training methods for deep networks often suffer from …

We posit that a reinforcement learning (RL) agent will perform better when it uses
representations that are better at predicting the future, particularly in terms of few-shot …

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" …

To better understand catastrophic forgetting, we study fitting an overparameterized linear
model to a sequence of tasks with different input distributions. We analyze how much the …