Few-shot class-incremental learning (FSCIL) aims to learn progressively about new classes with very few labeled samples, without forgetting the knowledge of already learnt classes. FSCIL suffers from two major challenges: (i) over-fitting on the new classes due to limited amount of data, (ii) catastrophically forgetting about the old classes due to unavailability of data from these classes in the incremental stages. In this work, we propose a self-supervised stochastic classifier (S3C) to counter both these challenges in FSCIL. The stochasticity of the classifier weights (or class prototypes) not only mitigates the adverse effect of absence of large number of samples of the new classes, but also the absence of samples from previously learnt classes during the incremental steps. This is complemented by the self-supervision component, which helps to learn features from the base classes which generalize well to unseen classes that are encountered in future, thus reducing catastrophic forgetting. Extensive evaluation on three benchmark datasets using multiple evaluation metrics show the effectiveness of the proposed framework. We also experiment on two additional realistic scenarios of FSCIL, namely where the number of annotated data available for each of the new classes can be different, and also where the number of base classes is much lesser, and show that the proposed S3C performs significantly better than the state-of-the-art for all these challenging scenarios.

翻译：少样本增量类学习（FSCIL）旨在利用极少量标注样本逐步学习新类别，同时不遗忘已学类别的知识。FSCIL面临两大挑战：（i）因数据量有限导致对新类别的过拟合；（ii）在增量阶段因旧类别数据不可用而导致的灾难性遗忘。本文提出一种自监督随机分类器（S3C）以应对这两大挑战。分类器权重（或类原型）的随机性不仅减轻了新类别样本数量不足的负面影响，还缓解了增量阶段缺乏先前已学类别样本的问题。自监督组件进一步增强了该方法的有效性，它有助于从基类中学习能够良好泛化至未来未知类别的特征，从而减少灾难性遗忘。在三个基准数据集上的多指标评估充分证明了该框架的有效性。我们还针对FSCIL的两种额外现实场景进行实验：新类别标注样本数量各不相同的场景以及基类数量显著减少的场景，结果表明，所提出的S3C在所有具有挑战性的场景中均显著优于现有最优方法。