首次会话适应：一种用于类增量学习的强无回放基线
First Session Adaptation: A Strong Replay-Free Baseline for Class-Incremental Learning
AI总结：
本文提出首次会话适应（FSA）基线，仅在首次会话适应网络主体并固定，之后用LDA头进行精确增量更新，无需回放，在15/16种CIL设置中显著超越现有方法。
摘要：
在类增量学习（Class-Incremental Learning, CIL）中，图像分类系统在每个学习会话中都会接触到新的类别，并且必须进行增量更新。针对这一问题的方法在CIL的每个会话中都会同时更新分类头和特征提取器主体。在本工作中，我们提出了一种基线方法，称为首次会话适应（First Session Adaptation, 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适应和主体适应对性能的相对贡献。FSA仅在第一个学习会话中适应预训练神经网络的主体，之后将其固定；然后，在适应后的主体之上放置一个基于线性判别分析（linear discriminant analysis, LDA）的头，从而允许在CIL过程中进行精确更新。FSA是无回放的，即它不会存储来自持续学习先前会话的样本。为了从经验上验证FSA的动机，我们首先考虑了22个多样化的图像分类数据集，在高样本/低样本离线设置下评估不同的头部和主体适应技术。我们发现LDA头表现良好，并且能够开箱即用地支持CIL。我们还发现，特征级层调制（Featurewise Layer Modulation, 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非常有效。其次，我们从经验上研究了各种CIL设置，包括高样本CIL和少样本CIL，其中包含文献中先前使用过的设置。我们表明，在所考虑的16种设置中，FSA在15种设置上显著优于当前最先进的方法。带有FiLM适配器的FSA在少样本设置中尤其表现出色。这些结果表明，当前针对连续主体适应的方法并未如预期那样发挥作用。最后，我们提出了一种可应用于一组未标注输入的度量，该度量能够预测主体适应所带来的收益。
英文摘要：
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.
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