Few-shot class-incremental learning (FSCIL) aims to incrementally fine-tune a model
(trained on base classes) for a novel set of classes using a few examples without forgetting …

Real-world industrial scenarios pose a challenging task known as few-shot class-
incremental learning (FSCIL), which aims to recognize new classes using a few samples …

This paper investigates a new, practical, but challenging problem named Non-exemplar
Online Class-incremental continual Learning (NO-CL), which aims to preserve the …

In response to the rapidly evolving nature of adversarial attacks on a monthly basis,
numerous defenses have been proposed to generalize against as many known attacks as …

In the few-shot class-incremental learning, new class samples are utilized to learn the
characteristics of new classes, while old class exemplars are used to avoid old knowledge …

Few-Shot Class-Incremental Learning (FSCIL) models aim to incrementally learn new
classes with scarce samples while preserving knowledge of old ones. Existing FSCIL …

Recently, few-shot learning (FSL) has received increasing attention because of difficulties in
sample collection in some application scenarios, such as maritime surveillance using …

Lifelong learning offers a promising paradigm of building a generalist agent that learns and
adapts over its lifespan. Unlike traditional lifelong learning problems in image and text …

Abstract Knowledge distillation (KD) aims to build a lightweight deep neural network model
under the guidance of a large-scale teacher model for model simplicity. Despite improved …

FSCIL (Few-shot class-incremental learning) is a prominent research topic in the ML
community. It faces two significant challenges: forgetting old class knowledge and overfitting …