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Building up reliable Out-of-Distribution (OOD) detectors is challenging,often requiring the use of OOD data during training. In this work, we develop adata-driven approach which is distinct and complementary to existing works:Instead of using external OOD data, we fully exploit the internalin-distribution (ID) training set by utilizing generative models to produceadditional synthetic ID images. The classifier is then trained using a novelobjective that computes weighted loss on real and synthetic ID samplestogether. Our training framework, which is termed SIO, serves as a"plug-and-play" technique that is designed to be compatible with existing andfuture OOD detection algorithms, including the ones that leverage available OODtraining data. Our experiments on CIFAR-10, CIFAR-100, and ImageNet variantsdemonstrate that SIO consistently improves the performance of nearly allstate-of-the-art (SOTA) OOD detection algorithms. For instance, on thechallenging CIFAR-10 v.s. CIFAR-100 detection problem, SIO improves the averageOOD detection AUROC of 18 existing methods from 86.25\% to 89.04\% and achievesa new SOTA of 92.94\% according to the OpenOOD benchmark. Code is available athttps://github.com/zjysteven/SIO.

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