Out-of-distribution (OOD) detection plays a crucial role as a mechanism for handling anomalies in computer vision systems. Among existing approaches, outlier exposure (OE), which trains the model with an additional auxiliary OOD dataset, has demonstrated strong effectiveness. However, acquiring clean and well-curated auxiliary OOD data is often infeasible, particularly within large and complex systems. Alternatively, wild outliers, i.e., unlabeled samples collected directly in deployment environments, are abundant and easy to obtain, and recent studies have shown that they can substantially benefit OOD detection learning. Nevertheless, wild outliers typically contain a mixture of in-distribution (ID) and OOD samples. Directly using them as auxiliary OOD data unavoidably exposes the model to adverse supervision signals arising from the contained ID samples. Yet existing methods still lack an effective strategy that can fully leverage wild outliers while suppressing the negative influence introduced by their ID subset. To this end, we propose a simple yet effective method named Clustering for Wild Outlier Exposure (C-WOE), which alleviates the adverse effect of the ID samples contained within wild outliers by reweighting them. Specifically, C-WOE assigns higher weights to real OOD samples and lower weights to ID samples and dynamically updates these weights during training. Theoretically, we establish solid guarantees for the proposed method. Empirically, extensive experiments conducted on various real-world benchmarks and simulated datasets demonstrate that C-WOE notably achieves superior performance compared with state-of-the-art methods, validating its reliability in image processing applications.

To ensure the reliability and security of machine learning classification models when deployed in the open world, it is crucial that these models can detect out-of-distribution (OOD) data that exhibits semantic shifts from the in-distribution (ID) data used during training. This necessity has spurred extensive research on OOD detection. Previous methods required a large amount of finely labeled OOD data for model training, which is costly or performed poorly in open-world scenarios. To address these limitations, we propose a novel method named focus on important samples (FIS) in this paper. FIS leverages model-predicted OOD scores to identify and focus on important samples that are more beneficial for model training. By learning from these important samples, our method aims to achieve reliable OOD detection performance while reducing training costs and the risk of overfitting training data, thereby enabling the model to better distinguish between ID and OOD data. Extensive experiments across diverse OOD detection scenarios demonstrate that FIS achieves superior performance compared to existing approaches, highlighting its robust and efficient OOD detection performance in practical applications.

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 OOD data during 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.