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 …

Continual learning (CL) learns a sequence of tasks incrementally with the goal of achieving
two main objectives: overcoming catastrophic forgetting (CF) and encouraging knowledge …

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 …

Abstract Replay strategies are Continual Learning techniques which mitigate catastrophic
forgetting by keeping a buffer of patterns from previous experiences, which are interleaved …

Continual learning studies agents that learn from streams of tasks without forgetting previous
ones while adapting to new ones. Two recent continual-learning scenarios have opened …

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

Continual learning (CL) of a sequence of tasks is often accompanied with the catastrophic
forgetting (CF) problem. Existing research has achieved remarkable results in overcoming …

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 …

Catastrophic forgetting is a key obstacle to continual learning. One of the state-of-the-art
approaches is orthogonal projection. The idea of this approach is to learn each task by …

One of the main motivations of studying continual learning is that the problem setting allows
a model to accrue knowledge from past tasks to learn new tasks more efficiently. However …