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 …

This chapter provides approaches to the problem of quantizing the numerical values in deep
Neural Network computations, covering the advantages/disadvantages of current methods …

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 …

This paper considers incremental few-shot learning, which requires a model to continually
recognize new categories with only a few examples provided. Our study shows that existing …

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

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 …

Few-shot class incremental learning (FSCIL) aims to incrementally add sets of novel classes
to a well-trained base model in multiple training sessions with the restriction that only a few …

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 …

Few-shot class incremental learning--the problem of updating a trained classifier to
discriminate among an expanded set of classes with limited labeled data--is a key challenge …