A Simple Framework for Robust Out-of-Distribution Detection

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Out-of-distribution (OOD) detection, i.e., identifying whether a given test sample is drawn from outside the training distribution, is essential for a deep classifier to be deployed in a real-world application. The existing state-of-the-art methods of OOD detection tackle this issue by utilizing the internal feature of the classification network. However, we found that such detection methods inherently struggle to detect hard OOD images, i.e., drawn near from the training distribution: a naive softmax-based baseline even outperforms them. Motivated by this, we propose a simple yet effective training scheme for further calibrating the softmax probability of a classifier to achieve high OOD detection performance under both hard and easy scenarios. In particular, we suggest to optimize consistency regularization and self-supervised loss during training. Our experiments demonstrate the superiority of our simple method under various OOD detection scenarios.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2022
Language
English
Article Type
Article
Citation

IEEE ACCESS, v.10, pp.23086 - 23097

ISSN
2169-3536
DOI
10.1109/ACCESS.2022.3153723
URI
http://hdl.handle.net/10203/294827
Appears in Collection
AI-Journal Papers(저널논문)
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