C-WOE: Clustering for Out-of-Distribution Detection Learning With Wild Outlier Exposure

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.