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 …

Deep neural networks are susceptible to catastrophic forgetting when trained on sequential
tasks. Various continual learning (CL) methods often rely on exemplar buffers or/and …

In the scenario of class-incremental learning (CIL), deep neural networks have to adapt their
model parameters to non-stationary data distributions, eg, the emergence of new classes …

Class-incremental learning (CIL) aims to train a model to learn new classes from non-
stationary data streams without forgetting old ones. In this paper, we propose a new kind of …

The pursuit of long-term autonomy mandates that robotic agents must continuously adapt to
their changing environments and learn to solve new tasks. Continual learning seeks to …

Recently, a new Continual Learning (CL) paradigm was presented to control catastrophic
forgetting, called Interval Continual Learning (InterContiNet), which relies on enforcing …

Improving model prediction capability based on continuously collected data is one of the
biggest challenges in a mature, intelligent weather forecasting system. To tackle this …