4 How to lose the impression from spurious relationship for OOD recognition?
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, which is one to aggressive recognition means derived from the newest model output (logits) and it has found superior OOD detection abilities more than in person by using the predictive confidence rating. 2nd, we provide an inflatable evaluation using a larger collection out of OOD rating properties within the Point
The outcomes in the previous section obviously quick practical question: how do we most useful discover spurious and you can non-spurious OOD inputs when the education dataset contains spurious correlation? In this point, we totally look at common OOD detection steps, and show that feature-created methods enjoys a competitive edge within the improving non-spurious OOD identification, while you are discovering spurious OOD stays problematic (and therefore we subsequent describe officially inside the Area 5 ).
Feature-oriented vs. Output-dependent OOD Identification.
suggests that OOD identification gets challenging getting efficiency-depending tips specially when the education put include higher spurious correlation.