Machine Learning (ML) models struggle with data that changes over time or across domains due to factors such as noise, occlusion, illumination, or frequency, unlike humans who can learn from such non independent and identically distributed data. Consequently, a Continual Learning (CL) approach is indispensable, particularly, Domain-Incremental Learning. In this paper, we propose a novel pipeline for identifying tasks in domain-incremental learning scenarios without supervision. The pipeline comprises four steps. First, we obtain base embeddings from the raw data using an existing transformer-based model. Second, we group the embedding densities based on their similarity to obtain the nearest points to each cluster centroid. Third, we train an incremental task classifier using only these few points. Finally, we leverage the lightweight computational requirements of the pipeline to devise an algorithm that decides in an online fashion when to learn a new task using the task classifier and a drift detector. We conduct experiments using the SODA10M real-world driving dataset and several CL strategies. We demonstrate that the performance of these CL strategies with our pipeline can match the ground-truth approach, both in classical experiments assuming task boundaries, and also in more realistic task-agnostic scenarios that require detecting new tasks on-the-fly

本研究提出了一种新的无监督域增量学习识别任务的流程，该流程包括从原始数据中获取基础嵌入，根据它们与每个聚类中心的相似度获取最近的点，使用这些点训练增量任务分类器，并通过任务分类器和漂移检测器设计了一种算法来决定何时在线学习新任务。

TADIL：通过使用Transformer最近中心嵌入进行任务ID推断的任务无关域增量学习