Although not, early in the day steps generally focused on seminar low-spurious OOD
et al. [ lin2021mood ] and advised dynamic OOD inference framework one to increased the new computational overall performance off OOD recognition. I expose a special formalization of OOD recognition you to encapsulates one another spurious and you will non-spurious OOD research.
A parallel line regarding methods resort so you can generative activities [ goodfellow2014generative , kingma2018glow ] one to personally estimate inside-shipments density [ nalisnick2019deep , ren2019likelihood , serra2019input , xiao2020likelihood , kirichenko2020normalizing ] . Specifically, ren2019likelihood handled distinguishing between record and semantic blogs lower than unsupervised generative models. Generative methods produce restricting abilities compared to overseen discriminative habits owed towards the insufficient label guidance and you may generally speaking experience higher computational complexity. Notably, nothing of earlier performs systematically investigate brand new dictate away from spurious correlation getting OOD identification. All of our performs gifts a novel direction having defining OOD analysis and you can looks at brand new impact off spurious correlation from the education lay. Furthermore, our very own ingredients is far more general and you can wide compared to the visualize record (particularly, intercourse bias within CelebA tests is another particular contextual bias past picture history).
Near-ID Reviews.
All of our suggested spurious OOD can be viewed as a kind of near-ID testing. Orthogonal to our work, earlier work [ winkens2020contrastive , roy2021does ] felt brand new close-ID instances when the fresh new semantics regarding OOD inputs act like that ID studies (e.g.
, CIFAR-ten against. CIFAR-100). Inside our setting, spurious OOD enters could have different semantic labels but they are statistically around the ID data due to mutual environmental provides (