Unsupervised Out-of-Distribution Detection with Batch Normalization [article]

Jiaming Song and Yang Song and Stefano Ermon
2019 arXiv   pre-print
Likelihood from a generative model is a natural statistic for detecting out-of-distribution (OoD) samples. However, generative models have been shown to assign higher likelihood to OoD samples compared to ones from the training distribution, preventing simple threshold-based detection rules. We demonstrate that OoD detection fails even when using more sophisticated statistics based on the likelihoods of individual samples. To address these issues, we propose a new method that leverages batch
more » ... malization. We argue that batch normalization for generative models challenges the traditional i.i.d. data assumption and changes the corresponding maximum likelihood objective. Based on this insight, we propose to exploit in-batch dependencies for OoD detection. Empirical results suggest that this leads to more robust detection for high-dimensional images.
arXiv:1910.09115v1 fatcat:dggjj4ldwng2xfwqqqkkqppene