Out-of-distribution detection using normalizing flows on the data manifold

• URL: http://arxiv.org/abs/2308.13792v1
• Date: Sat, 26 Aug 2023 07:35:16 GMT
• Title: Out-of-distribution detection using normalizing flows on the data manifold
• Authors: Seyedeh Fatemeh Razavi, Mohammad Mahdi Mehmanchi, Reshad Hosseini, Mostafa Tavassolipour
• Abstract summary: This study investigates the effect of manifold learning using normalizing flows on out-of-distribution detection. We show that manifold learning improves the out-of-distribution detection ability of a class of likelihood-based models known as normalizing flows.
• Score: 3.725042082196983
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A common approach for out-of-distribution detection involves estimating an underlying data distribution, which assigns a lower likelihood value to out-of-distribution data. Normalizing flows are likelihood-based generative models providing a tractable density estimation via dimension-preserving invertible transformations. Conventional normalizing flows are prone to fail in out-of-distribution detection, because of the well-known curse of dimensionality problem of the likelihood-based models. According to the manifold hypothesis, real-world data often lie on a low-dimensional manifold. This study investigates the effect of manifold learning using normalizing flows on out-of-distribution detection. We proceed by estimating the density on a low-dimensional manifold, coupled with measuring the distance from the manifold, as criteria for out-of-distribution detection. However, individually, each of them is insufficient for this task. The extensive experimental results show that manifold learning improves the out-of-distribution detection ability of a class of likelihood-based models known as normalizing flows. This improvement is achieved without modifying the model structure or using auxiliary out-of-distribution data during training.

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