AI RESEARCH
Improving Model Safety by Targeted Error Correction
arXiv CS.CV
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ArXi:2605.02544v1 Announce Type: cross The widespread adoption of machine learning in critical applications demands techniques to mitigate high-consequence errors. Our method utilizes a dual-classifier GBDT pipeline to distinguish routine human-like errors from high-risk non-human misclassifications. Evaluated across three domains, animal breed classification, skin lesion diagnosis (ISIC 2018), and prostate histopathology (SICAPv2), our framework nstrates robust safety improvements.