  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.
