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 …

Continual learning is an emerging research challenge in machine learning that addresses
the problem where models quickly fit the most recently trained-on data but suffer from …

Session-based recommendation has received growing attention recently due to the
increasing privacy concern. Despite the recent success of neural session-based …

Efficient continual learning in humans is enabled by a rich set of neurophysiological
mechanisms and interactions between multiple memory systems. The brain efficiently …

Humans excel at lifelong learning, as the brain has evolved to be robust to distribution shifts
and noise in our ever-changing environment. Deep neural networks (DNNs), however …

Abstract Few-Shot Class Incremental Learning (FSCIL) is a recently introduced Class
Incremental Learning (CIL) setting that operates under more constrained assumptions: only …

Existing continual learning techniques focus on either task incremental learning (TIL) or
class incremental learning (CIL) problem, but not both. CIL and TIL differ mainly in that the …

Abstract The goal of General Continual Learning (GCL) is to preserve learned knowledge
and learn new knowledge with constant memory from an infinite data stream where task …

Continual learning (CL) in the brain is facilitated by a complex set of mechanisms. This
includes the interplay of multiple memory systems for consolidating information as posited …

Exemplar rehearsal-based methods with knowledge distillation (KD) have been widely used
in class incremental learning (CIL) scenarios. However, they still suffer from performance …