Few-shot Class-Incremental Learning (FSCIL) presents a unique challenge in machine
learning, as it necessitates the continuous learning of new classes from sparse labeled …

A significant challenge in achieving ubiquitous Artificial Intelligence is the limited ability of
models to rapidly learn new information in real-world scenarios where data follows long …

Recently, foundation language models (LMs) have marked significant achievements in the
domains of natural language processing (NLP) and computer vision (CV). Unlike traditional …

We address the Continual Learning (CL) problem, where a model has to learn a sequence
of tasks from non-stationary distributions while preserving prior knowledge as it encounters …

Online continual learning is a challenging problem where models must learn from a non-
stationary data stream while avoiding catastrophic forgetting. Inter-class imbalance during …

Continual learning is a promising machine learning paradigm to learn new tasks while
retaining previously learned knowledge over streaming training data. Till now, rehearsal …

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

Continual learning, also known as lifelong learning, addresses the fundamental challenge of
enabling machine learning models to learn from a continuous stream of data and adapt their …

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