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，必须进行增量更新。现有的方法在每个CIL阶段同时更新分类头和特征提取器主体。本文提出了一种基线方法——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和主体适应的相对性能贡献。FSA仅在第一个学习阶段对预训练的神经网络主体进行适应，之后将其固定；然后在适应后的主体上放置基于线性判别分析（LDA）的头部，从而通过CIL实现精确更新。FSA是无重放的，即它不会记忆之前持续学习会话中的示例。为从经验上验证FSA，我们首先考虑了22个图像分类数据集的多样化选择，在高低样本离线设置下评估了不同的头部和主体适应技术。我们发现LDA头部表现良好，可直接支持CIL。我们还发现特征级层调制（FiLM）适配器在少样本设置中非常有效，而全主体适应在高样本设置中表现优异。其次，我们实证研究了包括高样本CIL和少样本CIL在内的各种CIL设置，涵盖文献中已有使用的设置。结果表明，在所考虑的16个设置中，FSA在15个设置上显著优于现有最优方法。带有FiLM适配器的FSA在少样本设置中尤其出色。这些结果指出，当前持续主体适应的方法并未按预期工作。最后，我们提出了一种可应用于未标注输入集的度量方法，该度量能预测主体适应的收益。