This paper investigates a new, practical, but challenging problem named Non-exemplar Online Class-incremental continual Learning (NO-CL), which aims to preserve the discernibility of base classes without buffering data examples and efficiently learn novel classes continuously in a single-pass (i.e., online) data stream. The challenges of this task are mainly two-fold: (1) Both base and novel classes suffer from severe catastrophic forgetting as no previous samples are available for replay. (2) As the online data can only be observed once, there is no way to fully re-train the whole model, e.g., re-calibrate the decision boundaries via prototype alignment or feature distillation. In this paper, we propose a novel Dual-prototype Self-augment and Refinement method (DSR) for NO-CL problem, which consists of two strategies: 1) Dual class prototypes: vanilla and high-dimensional prototypes are exploited to utilize the pre-trained information and obtain robust quasi-orthogonal representations rather than example buffers for both privacy preservation and memory reduction. 2) Self-augment and refinement: Instead of updating the whole network, we optimize high-dimensional prototypes alternatively with the extra projection module based on self-augment vanilla prototypes, through a bi-level optimization problem. Extensive experiments demonstrate the effectiveness and superiority of the proposed DSR in NO-CL.

翻译：本文研究了一个新颖、实用但具有挑战性的问题——无样本在线类增量持续学习（NO-CL），其目标是在不缓存数据样本的情况下保持基类的可辨别性，并在单遍（即在线）数据流中高效地持续学习新类。该任务的挑战主要体现在两方面：（1）由于无法重放先前样本，基类和新类均遭受严重的灾难性遗忘；（2）由于在线数据只能被观测一次，无法对整个模型进行完全重新训练，例如通过原型对齐或特征蒸馏重新校准决策边界。本文提出了一种用于NO-CL问题的双原型自增强与精炼方法（DSR），包含两种策略：（1）双类原型：利用普通原型和高维原型来利用预训练信息，获得鲁棒的准正交表示，而非样本缓存，从而实现隐私保护和内存缩减；（2）自增强与精炼：不同于更新整个网络，我们通过一个双层优化问题，基于自增强的普通原型，交替优化高维原型与额外的投影模块。大量实验证明了所提DSR方法在NO-CL中的有效性和优越性。