Mixture Outlier Exposure: Towards Out-of-Distribution Detection in Fine-grained Environments-Reference-Cited by-同舟云学术

Mixture Outlier Exposure: Towards Out-of-Distribution Detection in Fine-grained Environments

Published:2023-01 Issue: Volume: Page:
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Container-title:2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
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Author:

Zhang Jingyang¹,Inkawhich Nathan²,Linderman Randolph¹,Chen Yiran¹,Li Hai¹

Affiliation:

1. Duke University

2. Air Force Research Laboratory

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10030081/10030084/10030810.pdf?arnumber=10030810

Reference50 articles.

1. Fine-grained visual recognition with batch confusion norm;hsu,2019

2. Deep anomaly detection with outlier exposure;hendrycks;International Conference on Learning Representations,2018

3. On the importance of gradients for detecting distributional shifts in the wild;huang,2021

4. Generalized ODIN: Detecting Out-of-Distribution Image Without Learning From Out-of-Distribution Data

5. A baseline for detecting misclassified and out-of-distribution examples in neural networks;hendrycks;International Conference on Learning Representations,2017

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