EAT: Towards Long-Tailed Out-of-Distribution Detection

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Abstract

Despite recent advancements in out-of-distribution (OOD) detection, most current studies assume a class-balanced in-distribution training dataset, which is rarely the case in real-world scenarios. This paper addresses the challenging task of long-tailed OOD detection, where the in-distribution data follows a long-tailed class distribution. The main difficulty lies in distinguishing OOD data from samples belonging to the tail classes, as the ability of a classifier to detect OOD instances is not strongly correlated with its accuracy on the in-distribution classes. To overcome this issue, we propose two simple ideas: (1) Expanding the in-distribution class space by introducing multiple abstention classes. This approach allows us to build a detector with clear decision boundaries by training on OOD data using virtual labels. (2) Augmenting the context-limited tail classes by overlaying images onto the context-rich OOD data. This technique encourages the model to pay more attention to the discriminative features of the tail classes. We provide a clue for separating in-distribution and OOD data by analyzing gradient noise. Through extensive experiments, we demonstrate that our method outperforms the current state-of-the-art on various benchmark datasets. Moreover, our method can be used as an add-on for existing long-tail learning approaches, significantly enhancing their OOD detection performance. Code is available at: https://github.com/Stomachache/Long-Tailed-OOD-Detection.

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APA

Wei, T., Wang, B. L., & Zhang, M. L. (2024). EAT: Towards Long-Tailed Out-of-Distribution Detection. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 38, pp. 15787–15795). Association for the Advancement of Artificial Intelligence. https://doi.org/10.1609/aaai.v38i14.29508

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