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 information or knowledge.
While the existing surveys on forgetting have primarily focused on continual learning …

Continual learning aims to learn from a stream of continuously arriving data with minimum
forgetting of previously learned knowledge. While previous works have explored the …

In this work, we propose a novel prior learning method for advancing generalization and
uncertainty estimation in deep neural networks. The key idea is to exploit scalable and …

Most of the dominant approaches to continual learning are based on either memory replay,
parameter isolation, or regularization techniques that require task boundaries to calculate …

Class-incremental continual learning is an important area of research, as static deep
learning methods fail to adapt to changing tasks and data distributions. In previous works …

Catastrophic forgetting remains a core challenge in continual learning (CL), where the
models struggle to retain previous knowledge when learning new tasks. While existing …

In a typical Continual Learning (CL) setting, the goal is to learn a sequence of tasks that are
presented once while maintaining performance on all previously learned tasks. Current state …

The utility of machine learning for enhancing human well-being and health has risen to the
core discussion in both research and real-world application in today's technological front …