ワイルドな分布外検出を克服するための2つの側面について

Out-of-distribution (OOD) detection serves as an unknown-handling mechanism for open-world classification, enabling the identification of OOD data that diverge semantically from in-distribution (ID) data. The learning strategy known as outlier exposure (OE) enhances this process by incorporating OODdataduring model training, directly making models learn to discern between ID and OOD patterns. However, in practice, the collected OOD data often contain many ID semantics, of which the scenario is commonly referred to as wild OOD detection. It can markedly compromise the reliability of models in OOD detection, yet few studies have addressed this critical issue. In this paper, we theoretically analyze wild OOD detection from the instance and distribution facets, respectively, to better comprehend its challenges and accordingly introduce two general solutions. At the instance facet, ID/OOD indicators contain errors due to the wild nature, where some data are of OOD labels yet should be assigned as ID. Hence, we introduce a general framework that can dynamically estimate the true ID/OOD indicators solely based on wild OOD data, thereby mitigating their negative impacts. At the distribution facet, the wild OOD distribution is a mixture of ID and OOD distributions, where the ID sub-distribution can mislead the model. We therefore propose a resampling scheme to remove the potential ID sub distribution, with resampling probabilities estimated from the known ID distribution, enabling OE training to better address wild OOD detection. We provide theoretical guarantees for both solutions and develop algorithms that enhance their practical efficacy, ultimately integrating them into a unified framework that leverages their complementary strengths. Ultimately, we validate our approaches through comprehensive empirical evaluations across a range of wild OOD detection scenarios, clearly demonstrating the superior performance and reliability of our methods when compared to advanced counterparts.