arXiv AI By Timm Hess, Abhishek Jha, Gido M. van de Ven, Tinne Tuytelaars

Forgetting, plasticity, and co-observation: a third facet of continual learning

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The paper argues that catastrophic forgetting and loss of plasticity alone cannot explain why naive sequential training underperforms offline joint training. It introduces data co-observation as a third factor, showing that observing training data together consistently improves performance across supervised and self-supervised settings. The study also reinterprets common continual learning methods, suggesting that memory replay’s success stems from restoring co-observation benefits rather than merely mitigating forgetting.

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arXiv AI
3d ago

On the Plasticity Collapse in Continual Machine Unlearning

The paper investigates continual machine unlearning, where models must forget data over time. It identifies a fundamental issue called plasticity collapse, where successive unlearning requests cause geometric constraints that saturate parameter space, leading to two failure modes: forward failure (reduced forgetting quality) and backward failure (re‑memorization). Experiments across architectures and datasets confirm that plasticity collapse is a pervasive problem in continual unlearning.

By Yingdan Shi, Xiang Xu, Kaize Ding, Alfred O. Hero, Ren Wang