Out of distribution reasoning by weakly-supervised disentangled logic variational autoencoder

Out-of-distribution (OOD) detection, i.e., finding test samples derived from a different distribution than the training set, as well as reasoning about such samples (OOD reasoning), are necessary to ensure the safety of results generated by machine learning models. Recently there have been promising...

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Main Authors: Rahiminasab, Zahra, Yuhas, Michael, Easwaran, Arvind
其他作者: College of Computing and Data Science
格式: Conference or Workshop Item
語言:English
出版: 2024
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在線閱讀:https://hdl.handle.net/10356/178684
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