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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Bibliographic Details
Main Authors: Rahiminasab, Zahra, Yuhas, Michael, Easwaran, Arvind
Other Authors: College of Computing and Data Science
Format: Conference or Workshop Item
Language:English
Published: 2024
Subjects:
Online Access:https://hdl.handle.net/10356/178684
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Institution: Nanyang Technological University
Language: English