In Class-Incremental Learning (CIL) an image classification system is exposed to new classes in each learning session and must be updated incrementally. Methods approaching this problem have updated both the classification head and the feature extractor body at each session of CIL. In this work, we develop a baseline method, First Session Adaptation (FSA), that sheds light on the efficacy of existing CIL approaches and allows us to assess the relative performance contributions from head and body adaption. FSA adapts a pre-trained neural network body only on the first learning session and fixes it thereafter; a head based on linear discriminant analysis (LDA), is then placed on top of the adapted body, allowing exact updates through CIL. FSA is replay-free i.e.~it does not memorize examples from previous sessions of continual learning. To empirically motivate FSA, we first consider a diverse selection of 22 image-classification datasets, evaluating different heads and body adaptation techniques in high/low-shot offline settings. We find that the LDA head performs well and supports CIL out-of-the-box. We also find that Featurewise Layer Modulation (FiLM) adapters are highly effective in the few-shot setting, and full-body adaption in the high-shot setting. Second, we empirically investigate various CIL settings including high-shot CIL and few-shot CIL, including settings that have previously been used in the literature. We show that FSA significantly improves over the state-of-the-art in 15 of the 16 settings considered. FSA with FiLM adapters is especially performant in the few-shot setting. These results indicate that current approaches to continuous body adaptation are not working as expected. Finally, we propose a measure that can be applied to a set of unlabelled inputs which is predictive of the benefits of body adaptation.

翻译：在类增量学习（CIL）中，图像分类系统在每个学习阶段都会接触到新类别，并需要增量式地更新。现有方法在每个CIL阶段均同时更新分类头和特征提取器主体。本文提出一种基线方法——首次会话适应（FSA），该方法揭示了现有CIL方法的有效性，并使我们能够评估头部与主体适应的相对性能贡献。FSA仅在第一学习阶段对预训练神经网络主体进行适应，之后固定该主体；在适应后的主体之上部署基于线性判别分析（LDA）的分类头，从而支持通过CIL进行精确更新。FSA无需回放，即不记忆先前连续学习阶段的样本。为从实验角度验证FSA，我们首先选取22个图像分类数据集，在高低样本量的离线场景中评估不同分类头与主体适应技术。我们发现LDA分类头性能优异，且可直接支持CIL；同时，特征级层调制（FiLM）适配器在小样本场景中效果显著，而全主体适应在大样本场景中表现突出。其次，我们通过实验系统研究了多种CIL设置，包括高样本量CIL与小样本CIL，涵盖文献中已有的设置。结果表明，在16种设置中，FSA在15种上显著超越当前最优方法。采用FiLM适配器的FSA在小样本场景中尤为高效。这些结果说明，当前连续主体适应方法并未如预期发挥作用。最后，我们提出一种可应用于未标注输入集合的度量标准，该度量能够预测主体适应的收益。