Abstract
Out-of-distribution (OOD) detection is necessary in various real-world applications. However, training artificial intelligence models exclusively on in-distribution (ID) data frequently results in misclassifying OOD samples as ID classes, leading to significant adverse consequences. Inspired by recent studies on spurious outlier generation, we propose two novel strategies for generating fake OOD data. The first strategy constructs fake OOD data from the perspective of foreground-background separation by a large vision language model, while the second strategy combines patches from different ID images to create a new composite image as fake OOD data. To further improve the model’s awareness of OOD data, we design a novel loss function and a novel scoring function from two different perspectives to separate ID from OOD data. Our method demonstrates superior OOD detection performance on three widely used benchmarks.
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Acknowledgement
This work is supported in part by the National Natural Science Foundation of China (grant No. 62071502), the Major Key Project of PCL (grant No. PCL2023A09), and Guangdong Excellent Youth Team Program (grant No. 2023B1515040025).
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Wang, P., Chen, J., Zhou, Y., Wang, R. (2025). EFOA: Enhancing Out-of-Distribution Detection by Fake Outlier Augmentation. In: Lin, Z., et al. Pattern Recognition and Computer Vision. PRCV 2024. Lecture Notes in Computer Science, vol 15033. Springer, Singapore. https://doi.org/10.1007/978-981-97-8502-5_7
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DOI: https://doi.org/10.1007/978-981-97-8502-5_7
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