Deep Neural Network (DNN) has achieved great success on datasets of closed class set. However, new classes, like new categories of social media topics, are continuously added to the real world, making it necessary to incrementally learn. This is hard for DNN because it tends to focus on fitting to new classes while ignoring old classes, a phenomenon known as catastrophic forgetting. State-of-the-art methods rely on knowledge distillation and data replay techniques but still have limitations. In this work, we analyze the causes of catastrophic forgetting in class incremental learning, which owes to three factors: representation drift, representation confusion, and classifier distortion. Based on this view, we propose a two-stage learning framework with a fixed encoder and an incrementally updated prototype classifier. The encoder is trained with self-supervised learning to generate a feature space with high intrinsic dimensionality, thus improving its transferability and generality. The classifier incrementally learns new prototypes while retaining the prototypes of previously learned data, which is crucial in preserving the decision boundary.Our method does not rely on preserved samples of old classes, is thus a non-exemplar based CIL method. Experiments on public datasets show that our method can significantly outperform state-of-the-art exemplar-based methods when they reserved 5 examplers per class, under the incremental setting of 10 phases, by 18.24% on CIFAR-100 and 9.37% on ImageNet100.

翻译：深度神经网络（DNN）在封闭类别集合的数据集上取得了巨大成功。然而，现实世界中会不断加入新类别（如社交媒体话题的新分类），这使得增量学习成为必要。由于DNN倾向于专注于拟合新类别而忽略旧类别（这一现象称为灾难性遗忘），因此增量学习对DNN而言颇具挑战性。现有最优方法依赖知识蒸馏与数据重放技术，但仍存在局限性。本文分析了类别增量学习中灾难性遗忘的成因，归结为三个因素：表征漂移、表征混淆与分类器失真。基于这一视角，我们提出一种两阶段学习框架，包含固定编码器与增量更新的原型分类器。编码器通过自监督学习训练，生成具有高内在维度的特征空间，从而提升其可迁移性与泛化能力。分类器在保留先前学习数据的原型的同时增量学习新类原型，这对于维持决策边界至关重要。本方法不依赖旧类样本的保存，因此是一种基于非样本的类别增量学习方法。在公开数据集上的实验表明，在10阶段增量设置下，当每类仅保留5个样本时，我们的方法在CIFAR-100与ImageNet100上分别以18.24%与9.37%的准确率优势显著超越基于样本的最新方法。