AI RESEARCH
Counterfactual Stress Testing for Image Classification Models
arXiv CS.CV
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ArXi:2605.10894v1 Announce Type: new Deep learning models in medical imaging often fail when deployed in new clinical environments due to distribution shifts in graphics, scanner hardware, or acquisition protocols. A central challenge is underspecification, where models with similar validation performance exhibit divergent real-world failure modes.