Papers › A Bag of Tricks for Few-Shot Class-Incremental Learning

A Bag of Tricks for Few-Shot Class-Incremental Learning

21 Mar 2024arXiv:2403.14392archive 2025-07-28

Shuvendu Roy, Chunjong Park, Aldi Fahrezi, Ali Etemad

We present a bag of tricks framework for few-shot class-incremental learning (FSCIL), which is a challenging form of continual learning that involves continuous adaptation to new tasks with limited samples. FSCIL requires both stability and adaptability, i.e., preserving proficiency in previously learned tasks while learning new ones. Our proposed bag of tricks brings together six key and highly influential techniques that improve stability, adaptability, and overall performance under a unified framework for FSCIL. We organize these tricks into three categories: stability tricks, adaptability tricks, and training tricks. Stability tricks aim to mitigate the forgetting of previously learned classes by enhancing the separation between the embeddings of learned classes and minimizing interference when learning new ones. On the other hand, adaptability tricks focus on the effective learning of new classes. Finally, training tricks improve the overall performance without compromising stability or adaptability. We perform extensive experiments on three benchmark datasets, CIFAR-100, CUB-200, and miniIMageNet, to evaluate the impact of our proposed framework. Our detailed analysis shows that our approach substantially improves both stability and adaptability, establishing a new state-of-the-art by outperforming prior works in the area. We believe our method provides a go-to solution and establishes a robust baseline for future research in this area.

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Tasks

Class Incremental Learning Continual Learning Few-Shot Class-Incremental Learning Incremental Learning class-incremental learning

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Few-Shot Class-Incremental Learning	CIFAR-100	BOT	Last Accuracy	58.75	#4 of 11	Archive leaderboard	report
Few-Shot Class-Incremental Learning	CUB-200-2011	BOT	Last Accuracy	63.75	#5 of 6	Archive leaderboard	report
Few-Shot Class-Incremental Learning	mini-Imagenet	BOT	Last Accuracy	59.57	#4 of 12	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

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