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arXiv Machine Learning By BoRen Deng, Xiangyue Ma, Chenglong Li, Xiaoting Du

What Must Replay Preserve? Separating Correctable Bias from Class Correspondence

Read the original on arXiv Machine Learning →

The paper investigates what information must be preserved in replay buffers for class‑incremental learning. By treating cached predictions as temporally heterogeneous supervision, the authors separate classes known at storage time from those learned later, and evaluate the impact of deleting logit matching. Experiments on CIFAR‑100 with DER++ show that a simple task‑level offset can largely correct the cost of removing later‑class matching, while the cost of disrupting class correspondence remains.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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