TrustMix paper proposes graceful degradation for pretrained models under distribution shift
A new arXiv paper, TrustMix, addresses how pretrained neural networks behave when predicting beyond their training domain, arguing such extrapolation requires explicit assumptions about the ground truth.
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- 2026-11-06 05:00 · arXiv cs.LG
TrustMix: Graceful Degradation for Pretrained Models under Distribution Shift