arXiv AI October 5, 2026 By Seongjun Lee, Changhee Lee

Safeguarding Mutual Correction in Source-Free Domain Adaptation via Cut Statistics

The paper introduces SafeCut, a method for Source-Free Domain Adaptation that uses Vision‑Language models as external knowledge. SafeCut employs a cut statistic to gauge prediction reliability, enabling a dynamic, reliability‑gated supervision between the source‑pretrained model and the ViL model. This approach selectively amplifies correct mutual corrections while suppressing error propagation, achieving state‑of‑the‑art performance on multiple SFDA benchmarks.

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