SIO: Synthetic In-Distribution Data Benefits Out-of-Distribution Detection

• URL: http://arxiv.org/abs/2303.14531v1
• Date: Sat, 25 Mar 2023 18:34:34 GMT
• Title: SIO: Synthetic In-Distribution Data Benefits Out-of-Distribution Detection
• Authors: Jingyang Zhang, Nathan Inkawhich, Randolph Linderman, Ryan Luley, Yiran Chen, Hai Li
• Abstract summary: We develop a data-driven approach to building reliable Out-of-Distribution (OOD) detectors. We exploit the internal in-distribution (ID) training set by utilizing generative models to produce additional synthetic ID images. Our training framework, which is termed SIO, serves as a "plug-and-play" technique that is designed to be compatible with existing and future OOD detection algorithms.
• Score: 34.97444309333315
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Building up reliable Out-of-Distribution (OOD) detectors is challenging, often requiring the use of OOD data during training. In this work, we develop a data-driven approach which is distinct and complementary to existing works: Instead of using external OOD data, we fully exploit the internal in-distribution (ID) training set by utilizing generative models to produce additional synthetic ID images. The classifier is then trained using a novel objective that computes weighted loss on real and synthetic ID samples together. Our training framework, which is termed SIO, serves as a "plug-and-play" technique that is designed to be compatible with existing and future OOD detection algorithms, including the ones that leverage available OOD training data. Our experiments on CIFAR-10, CIFAR-100, and ImageNet variants demonstrate that SIO consistently improves the performance of nearly all state-of-the-art (SOTA) OOD detection algorithms. For instance, on the challenging CIFAR-10 v.s. CIFAR-100 detection problem, SIO improves the average OOD detection AUROC of 18 existing methods from 86.25\% to 89.04\% and achieves a new SOTA of 92.94\% according to the OpenOOD benchmark. Code is available at https://github.com/zjysteven/SIO.

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