在无监督持续学习中整合当下与过去
Integrating Present and Past in Unsupervised Continual Learning
AI总结：
本研究构建无监督持续学习统一框架，指出现有方法忽略跨任务巩固的缺陷，提出在独立嵌入空间优化三目标的Osiris方法，在含两个新基准的所有测试中达最优，且证实模型可从更真实的学习场景获益。
摘要：
我们为无监督持续学习（UCL）构建了一个统一框架，该框架将针对当下数据和过去数据的学习目标解耦，涵盖稳定性、可塑性以及跨任务巩固三个维度。该框架揭示，现有许多UCL方法忽略了跨任务巩固，试图在共享嵌入空间中平衡可塑性与稳定性。由于缺乏任务内数据多样性且当前任务的学习效果下降，这会导致性能变差。我们提出的Osiris方法在独立的嵌入空间中显式优化所有三个目标，在所有基准测试上都取得了SOTA（当前最优）性能，其中包括本文提出的两个具有语义结构化任务序列的新型基准。与标准基准相比，这两个结构化基准更接近人类和动物在真实环境中导航时接收的视觉信号。最后，我们提供了一些初步证据，表明持续模型可以从这类更贴近现实的学习场景中获益。
英文摘要：
We formulate a unifying framework for unsupervised continual learning (UCL), which disentangles learning objectives that are specific to the present and the past data, encompassing stability, plasticity, and cross-task consolidation. The framework reveals that many existing UCL approaches overlook cross-task consolidation and try to balance plasticity and stability in a shared embedding space. This results in worse performance due to a lack of within-task data diversity and reduced effectiveness in learning the current task. Our method, Osiris, which explicitly optimizes all three objectives on separate embedding spaces, achieves state-of-the-art performance on all benchmarks, including two novel benchmarks proposed in this paper featuring semantically structured task sequences. Compared to standard benchmarks, these two structured benchmarks more closely resemble visual signals received by humans and animals when navigating real-world environments. Finally, we show some preliminary evidence that continual models can benefit from such realistic learning scenarios.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
