TrustMix: enabling graceful degradation for pretrained models under distribution shift
Read the original at arxiv.org→arXiv:2611.07206v1 Announce Type: new Abstract: Predicting beyond the training domain necessarily requires assumptions about how the ground truth extrapolates, yet in most neural networks these assumptions are made...
Original headline: "TrustMix: Graceful Degradation for Pretrained Models under Distribution Shift"
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- Nov 6, 05:00 UTC arXiv cs.LG lead source TrustMix: Graceful Degradation for Pretrained Models under Distribution Shift