Online continual learning aims to continuously train neural networks from a continuous data stream with a single pass-through data. As the most effective approach, the rehearsal-based methods replay part of previous data. Commonly used predictors in existing methods tend to generate biased dot-product logits that prefer to the classes of current data, which is known as a bias issue and a phenomenon of forgetting. Many approaches have been proposed to overcome the forgetting problem by correcting the bias; however, they still need to be improved in online fashion. In this paper, we try to address the bias issue by a more straightforward and more efficient method. By decomposing the dot-product logits into an angle factor and a norm factor, we empirically find that the bias problem mainly occurs in the angle factor, which can be used to learn novel knowledge as cosine logits. On the contrary, the norm factor abandoned by existing methods helps remember historical knowledge. Based on this observation, we intuitively propose to leverage the norm factor to balance the new and old knowledge for addressing the bias. To this end, we develop a heuristic approach called unbias experience replay (UER). UER learns current samples only by the angle factor and further replays previous samples by both the norm and angle factors. Extensive experiments on three datasets show that UER achieves superior performance over various state-of-the-art methods. The code is in https://github.com/FelixHuiweiLin/UER.

翻译：在线持续学习旨在从连续数据流中，通过单次数据遍历持续训练神经网络。作为最有效的方法，基于回放的机制会重放部分历史数据。现有方法中常用的预测器倾向于生成偏向当前数据类别的点积logits，这被称为偏差问题及遗忘现象。为克服遗忘问题，学界已提出多种通过纠正偏差来改进的方案，但它们在在线场景下的性能仍有待提升。本文尝试通过更直接高效的方法解决偏差问题。通过将点积logits分解为角度因子和范数因子，我们实验发现偏差问题主要存在于角度因子中——该因子可通过余弦logits学习新知识；而被现有方法舍弃的范数因子则有助于记忆历史知识。基于此观察，我们提出利用范数因子平衡新旧知识以消除偏差。为此，我们开发了一种称为无偏经验回放（UER）的启发式方法。UER仅通过角度因子学习当前样本，并通过范数与角度因子共同回放历史样本。在三个数据集上的大量实验表明，UER的性能优于多种最先进方法。代码详见https://github.com/FelixHuiweiLin/UER。