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.

我们提出了一种适用于无监督连续学习（UCL）的统一框架，该框架将实时数据和过去数据的特定学习目标区分开来，包括稳定性、可塑性和跨任务整合。我们的方法 Osiris 在分离的嵌入空间中明确优化这三个目标，并在所有基准测试中取得了最先进的性能，包括本文提出的两个具有语义结构化任务序列的新基准测试。与标准基准测试相比，这两个结构化基准更接近人类和动物在导航现实环境时接收到的视觉信号。最后，我们展示了一些初步的证据表明连续模型可以从这样的真实学习场景中受益。