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C-Score: Beyond Accuracy for Robustness Assessment in Semi-Supervised Learning under Open-World Unlabeled Contamination

This story is from 2026-08-24. It is preserved in the archive; the latest stories are on the live feed.

arXiv:2608.20667v1 Announce Type: new Abstract: Pseudo-label-based semi-supervised learning has achieved strong performance due to its simplicity and scalability. However, it is typically developed under a closed-world assumption that unlabeled data are drawn from the same distribution as labeled d…

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  1. 2026-08-24 04:00 · arXiv cs.LG
    C-Score: Beyond Accuracy for Robustness Assessment in Semi-Supervised Learning under Open-World Unlabeled Contamination

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