| Author | Search for: Wang, J.; Search for: Sun, S.1; Search for: Yu, Y. |
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| Affiliation | - National Research Council of Canada. Digital Technologies
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| Format | Text, Article |
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| Conference | 33rd Annual Conference on Neural Information Processing Systems, NeurIPS 2019, December 8-14, 2019, Vancouver, Canada |
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| Abstract | Novelty detection, a fundamental task in machine learning, has drawn a lot of recent attention due to its wide-ranging applications and the rise of neural approaches. In this work, we present a general framework for neural novelty detection that centers around a multivariate extension of the univariate quantile function. Our framework unifies and extends many classical and recent novelty detection algorithms, and opens the way to exploit recent advances in flow-based neural density estimation. We adapt the multiple gradient descent algorithm to obtain the first efficient end-to-end implementation of our framework that is free of tuning hyperparameters. Extensive experiments over a number of real datasets confirm the efficacy of our proposed method against state-of-the-art alternatives. |
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| Publication date | 2019-12-08 |
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| Publisher | Neural Information Processing Systems Foundation |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | 63b652a1-9f35-4c28-9b57-dde5b2b1fd29 |
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| Record created | 2021-04-06 |
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| Record modified | 2021-04-06 |
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