In continual learning (CL), an agent learns from a stream of tasks leveraging prior
experience to transfer knowledge to future tasks. It is an ideal framework to decrease the …

In continual learning (CL), an agent learns from a stream of tasks leveraging prior
experience to transfer knowledge to future tasks. It is an ideal framework to decrease the …

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 …

By learning a sequence of tasks continually, an agent in continual learning (CL) can improve
the learning performance of both a new task andold'tasks by leveraging the forward …

Lack of performance when it comes to continual learning over non-stationary distributions of
data remains a major challenge in scaling neural network learning to more human realistic …

Continual Learning (CL) aims to develop agents emulating the human ability to sequentially
learn new tasks while being able to retain knowledge obtained from past experiences. In this …

Continual learning aims to enable a single model to learn a sequence of tasks without
catastrophic forgetting. Top-performing methods usually require a rehearsal buffer to store …

Rehearsal is a critical component for class-incremental continual learning, yet it requires a
substantial memory budget. Our work investigates whether we can significantly reduce this …

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 …

We explore task-free continual learning (CL), in which a model is trained to avoid
catastrophic forgetting in the absence of explicit task boundaries or identities. Among many …