Label Smoothed Embedding Hypothesis for Out-of-Distribution Detection

• URL: http://arxiv.org/abs/2102.05131v1
• Date: Tue, 9 Feb 2021 21:04:44 GMT
• Title: Label Smoothed Embedding Hypothesis for Out-of-Distribution Detection
• Authors: Dara Bahri and Heinrich Jiang and Yi Tay and Donald Metzler
• Abstract summary: We propose an unsupervised method to detect OOD samples using a $k$-NN density estimate. We leverage a recent insight about label smoothing, which we call the emphLabel Smoothed Embedding Hypothesis We show that our proposal outperforms many OOD baselines and also provide new finite-sample high-probability statistical results.
• Score: 72.35532598131176
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Detecting out-of-distribution (OOD) examples is critical in many applications. We propose an unsupervised method to detect OOD samples using a $k$-NN density estimate with respect to a classification model's intermediate activations on in-distribution samples. We leverage a recent insight about label smoothing, which we call the \emph{Label Smoothed Embedding Hypothesis}, and show that one of the implications is that the $k$-NN density estimator performs better as an OOD detection method both theoretically and empirically when the model is trained with label smoothing. Finally, we show that our proposal outperforms many OOD baselines and also provide new finite-sample high-probability statistical results for $k$-NN density estimation's ability to detect OOD examples.

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