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 …

Large deep learning models are impressive, but they struggle when real-time data is not
available. Few-shot class-incremental learning (FSCIL) poses a significant challenge for …

Novel classes frequently arise in our dynamically changing world, eg, new users in the
authentication system, and a machine learning model should recognize new classes without …

In this paper, we tackle the problem of few-shot class incremental learning (FSCIL). FSCIL
aims to incrementally learn new classes with only a few samples in each class. Most existing …

Continually learning new classes from fresh data without forgetting previous knowledge of
old classes is a very challenging research problem. Moreover, it is imperative that such …

Few-shot class-incremental learning (FSCIL) aims at learning to classify new classes
continually from limited samples without forgetting the old classes. The mainstream …

Few-shot class-incremental learning (FSCIL) has been proposed aiming to enable a deep
learning system to incrementally learn new classes with limited data. Recently, a pioneer …

The continual appearance of new objects in the visual world poses considerable challenges
for current deep learning methods in real-world deployments. The challenge of new task …

Abstract Few-Shot Class-Incremental Learning (FSCIL) aims to continually learn novel
classes based on only few training samples, which poses a more challenging task than the …

Real-world scenarios are usually accompanied by continuously appearing classes with
scare labeled samples, which require the machine learning model to incrementally learn …