AI RESEARCH
Mitigating the ID-OOD Tradeoff in Open-Set Test-Time Adaptation
arXiv CS.CV
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ArXi:2604.01589v1 Announce Type: new Open-set test-time adaptation (OSTTA) addresses the challenge of adapting models to new environments where out-of-distribution (OOD) samples coexist with in-distribution (ID) samples affected by distribution shifts. In such settings, covariate shift-for example, changes in weather conditions such as snow-can alter ID samples, reducing model reliability. Consequently, models must not only correctly classify covariate-shifted ID (csID) samples but also effectively reject covariate-shifted OOD (csOOD) samples.