Exemplar-free class-incremental learning (CIL) poses several challenges since it prohibits
the rehearsal of data from previous tasks and thus suffers from catastrophic forgetting …

Few-shot class-incremental learning (FSCIL) is designed to incrementally recognize novel
classes with only few training samples after the (pre-) training on base classes with sufficient …

It is difficult to realize effective synthetic aperture radar (SAR) automatic target recognition
(ATR) in open scenarios because the ATR model cannot continuously learn from new …

Neural networks suffer from catastrophic forgetting when sequentially learning tasks phase-
by-phase, making them inapplicable in dynamically updated systems. Class-incremental …

Abstract In Class-Incremental Learning (CIL) an image classification system is exposed to
new classes in each learning session and must be updated incrementally. Methods …

Few-shot Class-Incremental Learning (FSCIL) aims at learning new concepts continually
with only a few samples, which is prone to suffer the catastrophic forgetting and overfitting …

Class-incremental few-shot learning, where new sets of classes are provided sequentially
with only a few training samples, presents a great challenge due to catastrophic forgetting of …

Given a model well-trained with a large-scale base dataset, few-shot class-incremental
learning (FSCIL) aims at incrementally learning novel classes from a few labeled samples …

With the rapid acquisition of remote sensing (RS) data, new categories of objects continue to
emerge, and some categories can only obtain a few training samples. Thus, few-shot class …

Few-shot continual learning is the ability to continually train a neural network from a
sequential stream of few-shot data. In this paper, we propose a Few-shot Continual Infomax …