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 eight 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.

翻译：我们提出了一种针对小样本类增量学习（FSCIL）的技巧集锦框架。FSCIL是一种具有挑战性的持续学习形式，要求模型在样本有限的条件下持续适应新任务。该任务需要同时兼顾稳定性与适应性，即在学习新任务的同时保持对先前学习任务的掌握能力。我们提出的技巧集锦整合了八项关键且影响深远的技巧，在统一的FSCIL框架下提升了稳定性、适应性和整体性能。我们将其分为三类：稳定性技巧、适应性技巧和训练技巧。稳定性技巧旨在通过增强已学习类别的嵌入分离度并最小化学习新类别时的干扰，来缓解对先前学习类别的遗忘。适应性技巧则专注于有效学习新类别。最后，训练技巧在不牺牲稳定性或适应性的前提下提升整体性能。我们在CIFAR-100、CUB-200和miniImageNet三个基准数据集上进行了广泛实验，评估了所提框架的效果。详细分析表明，我们的方法显著提升了稳定性与适应性，以超越现有研究成果的表现建立了新的最先进水平。我们相信，该方法为该领域未来研究提供了首选解决方案和稳健的基准参照。