Continual learning aims to learn a model from a continuous stream of data, but it mainly assumes a fixed number of data and tasks with clear task boundaries. However, in real-world scenarios, the number of input data and tasks is constantly changing in a statistical way, not a static way. Although recently introduced incremental learning scenarios having blurry task boundaries somewhat address the above issues, they still do not fully reflect the statistical properties of real-world situations because of the fixed ratio of disjoint and blurry samples. In this paper, we propose a new Stochastic incremental Blurry task boundary scenario, called Si-Blurry, which reflects the stochastic properties of the real-world. We find that there are two major challenges in the Si-Blurry scenario: (1) inter- and intra-task forgettings and (2) class imbalance problem. To alleviate them, we introduce Mask and Visual Prompt tuning (MVP). In MVP, to address the inter- and intra-task forgetting issues, we propose a novel instance-wise logit masking and contrastive visual prompt tuning loss. Both of them help our model discern the classes to be learned in the current batch. It results in consolidating the previous knowledge. In addition, to alleviate the class imbalance problem, we introduce a new gradient similarity-based focal loss and adaptive feature scaling to ease overfitting to the major classes and underfitting to the minor classes. Extensive experiments show that our proposed MVP significantly outperforms the existing state-of-the-art methods in our challenging Si-Blurry scenario.

翻译：持续学习旨在从连续数据流中学习模型，但主要假设数据与任务数量固定且任务边界清晰。然而，实际场景中输入数据与任务数量始终以统计方式而非静态方式变化。尽管近期引入的模糊任务边界增量学习场景部分解决了上述问题，但由于不重叠样本与模糊样本的比例固定，仍未能完全反映实际场景的统计特性。本文提出一种新的随机增量模糊任务边界场景（名为Si-Blurry），该场景能够反映真实世界的随机特性。我们发现Si-Blurry场景中存在两大挑战：（1）任务间与任务内遗忘问题；（2）类别不平衡问题。为解决这些问题，我们提出掩码与视觉提示调优方法（MVP）。在MVP中，为缓解任务间与任务内遗忘，我们提出一种新颖的实例级logit掩码与对比视觉提示调优损失，两者共同帮助模型区分当前批次中需要学习的类别，从而巩固已有知识。此外，针对类别不平衡问题，我们引入基于梯度相似性的焦点损失与自适应特征缩放，以缓解对主要类别的过拟合与对次要类别的欠拟合。大量实验表明，在具有挑战性的Si-Blurry场景下，我们提出的MVP方法显著优于现有最先进方法。