用知识适配器和伪任务学习，提升少样本增量学习的性能。
Knowledge Adaptation Network for Few-Shot Class-Incremental Learning
- 引入知识适配模块融合数据特有知识到通用表征中
- 通过模拟真实场景的伪任务学习，优化基类知识迁移
- 在多个数据集上实现领先效果，适合增量学习研究者
少样本类增量学习（FSCIL）旨在仅用少量样本识别新类别，同时保持对旧类别的识别性能。现有方法常采用均值特征初始化分类器权重，但新类别表征弱且存在偏差，导致效果不佳。本文提出知识适配网络（KANet），首先利用预训练的CLIP模型为每类提供通用表征；其次设计知识适配器（KA）模块，从训练数据中提取并融合数据特定知识至通用表征；此外，提出增量伪任务学习（IPEL）机制，通过模拟真实FSCIL过程来调整基类知识以适应新类。实验表明，该方法在CIFAR100、CUB200和ImageNet-R等多个数据集上表现优异，达到当前先进水平。
原文摘要 · Abstract (English)
Few-shot class-incremental learning (FSCIL) aims to incrementally recognize new classes using a few samples while maintaining the performance on previously learned classes. One of the effective methods to solve this challenge is to construct prototypical evolution classifiers. Despite the advancement achieved by most existing methods, the classifier weights are simply initialized using mean features. Because representations for new classes are weak and biased, we argue such a strategy is suboptimal. In this paper, we tackle this issue from two aspects. Firstly, thanks to the development of foundation models, we employ a foundation model, the CLIP, as the network pedestal to provide a general representation for each class. Secondly, to generate a more reliable and comprehensive instance representation, we propose a Knowledge Adapter (KA) module that summarizes the data-specific knowledge from training data and fuses it into the general representation. Additionally, to tune the knowledge learned from the base classes to the upcoming classes, we propose a mechanism of Incremental Pseudo Episode Learning (IPEL) by simulating the actual FSCIL. Taken together, our proposed method, dubbed as Knowledge Adaptation Network (KANet), achieves competitive performance on a wide range of datasets, including CIFAR100, CUB200, and ImageNet-R.
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