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Why Normalizing Flows Fail to DetectOut-of-Distribution Data

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Andrew Gordon Wilson: The inductive biases of normalizing flows can be more of a curse than a blessing. Our new paper, "Why Normalizing Flows Fail to Detect Out-of-Distribution Data", with @polkirichenko and @Pavel_Izmailov: https://arxiv.org/abs/2006.08545 https://t.co/BC1dTtexop

4 replies, 353 likes


Polina Kirichenko: Why Normalizing Flows Fail to Detect Out-of-Distribution Data https://arxiv.org/abs/2006.08545 We explore the inductive biases of normalizing flows based on coupling layers in the context of OOD detection (1/6)

2 replies, 340 likes


Polina Kirichenko: We will be presenting "Why Normalizing Flows Fail to Detect Out-of-Distribution Data" tomorrow 1pm EST at the INNF+ workshop #ICML2020! Will be very happy to chat about flows and OOD detection :)

2 replies, 110 likes


Pavel Izmailov: Excited to share this paper :) The high-level takeaway is that the main thing that affects OOD detection in likelihood-based models is the inductive biases. You can have the same likelihood on train and arbitrary likelihood outside train. Flows have bad biases for OOD detection. https://t.co/Rzl6Gr9Fy6

0 replies, 18 likes


Arnab Ray: https://arxiv.org/pdf/2006.08545.pdf What nepotism ? How many non-hereditary stars make their way into machine learning datasets ?(via Tuhin Chakraborty) https://t.co/Eomg1Cnvff

3 replies, 12 likes


ahghbfgfc: lmao varun dawan

0 replies, 6 likes


Iain Murray: Others' neat work on flow representations to check out: 1) Capturing low-dimensional manifolds, Brehmer and @KyleCranmer https://twitter.com/KyleCranmer/status/1268370127705309184 2) A critical look at flow representations and the inductive biases that result: @polkirichenko et al. https://twitter.com/polkirichenko/status/1272715634544119809

1 replies, 5 likes


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Found on Jun 16 2020 at https://arxiv.org/pdf/2006.08545.pdf

PDF content of a computer science paper: Why Normalizing Flows Fail to DetectOut-of-Distribution Data