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- W4385801223 abstract "Normalizing flows are generative models that show poor performance on out-of-distribution (OOD) detection tasks with a likelihood-based test. In this study we focus on the approximate mass metric. We show that while it improves OOD detection performance, it has limitations under a maximum likelihood training. To solve this limitation we modify the training objective by incorporating the approximate mass. It smooths the learnt distribution in the vicinity of training in-distribution data. We measure an average of 97.6% AUROC in our experiments on different benchmarks, showing an improvement of 16% with respect to the best baseline we tested against." @default.
- W4385801223 created "2023-08-15" @default.
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- W4385801223 date "2023-06-01" @default.
- W4385801223 modified "2023-09-25" @default.
- W4385801223 title "Improving Normalizing Flows with the Approximate Mass for Out-of-Distribution Detection" @default.
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- W4385801223 doi "https://doi.org/10.1109/cvprw59228.2023.00082" @default.
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