Continual learning (CL) provides a framework for training models in ever-evolving environments. Although re-occurrence of previously seen objects or tasks is common in real-world problems, the concept of repetition in the data stream is not often considered in standard benchmarks for CL. Unlike with the rehearsal mechanism in buffer-based strategies, where sample repetition is controlled by the strategy, repetition in the data stream naturally stems from the environment. This report provides a summary of the CLVision challenge at CVPR 2023, which focused on the topic of repetition in class-incremental learning. The report initially outlines the challenge objective and then describes three solutions proposed by finalist teams that aim to effectively exploit the repetition in the stream to learn continually. The experimental results from the challenge highlight the effectiveness of ensemble-based solutions that employ multiple versions of similar modules, each trained on different but overlapping subsets of classes. This report underscores the transformative potential of taking a different perspective in CL by employing repetition in the data stream to foster innovative strategy design.

翻译：持续学习为在持续演变的环境中训练模型提供了框架。尽管在现实问题中，先前见过的物体或任务会重复出现是普遍现象，但标准持续学习基准测试通常未考虑数据流中的重复概念。与基于缓冲区的策略中的回放机制不同——那里样本重复由策略控制，数据流中的重复源于环境本身。本报告总结了CVPR 2023的CLVision挑战赛，该赛事聚焦于类增量学习中的重复问题。报告首先概述了挑战目标，随后描述了决赛团队提出的三种旨在有效利用数据流中重复现象以实现持续学习的解决方案。挑战赛的实验结果凸显了基于集成方法的有效性，这些方法采用同一模块的多个版本，每个版本在不同但存在类别重叠的子集上训练。本报告强调了采用不同视角看待持续学习的变革潜力——通过利用数据流中的重复现象来推动创新策略设计。