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Detection of out-of-distribution (OOD) samples is crucial for safe real-world deployment of machine learning models.
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A simple unified framework for detecting out-of-distribution samples and adversarial attacks
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Weitang Liu, Xiaoyun Wang, John Owens, and Yixuan Li · 2020
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Deep semi-supervised anomaly detection
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Generalized out-of-distribution detection: A survey
Jingkang Yang, Kaiyang Zhou, Yixuan Li, and Ziwei Liu · 2021
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Towards unknown-aware learning with virtual outlier synthesis
Xuefeng Du, Zhaoning Wang, Mu Cai, and Sharon Li · 2022
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Zero-shot out-of-distribution detection based on the pre-trained model clip
Sepideh Esmaeilpour, Bing Liu, Eric Robertson, and Lei Shu · 2022
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Delving into out-of-distribution detection with vision-language representations
Yifei Ming, Ziyang Cai, Jiuxiang Gu, Yiyou Sun, Wei Li, and Yixuan Li · 2022
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Latent outlier exposure for anomaly detection with contaminated data
Chen Qiu, Aodong Li, Marius Kloft, Maja Rudolph, and Stephan Mandt · 2022
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The many faces of robustness: A critical analysis of out-of-distribution generalization
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Natural adversarial examples
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Learning transferable visual models from natural language supervision
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Panda: Adapting pretrained features for anomaly detection and segmentation
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Multiresolution knowledge distillation for anomaly detection
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Dice: Leveraging sparsification for out-of-distribution detection
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Out-of-distribution detection with deep nearest neighbors
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Non-parametric outlier synthesis
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Vim: Out-of-distribution with virtual-logit matching
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Clipood: Generalizing clip to out-of-distributions
Yang Shu, Xingzhuo Guo, Jialong Wu, Ximei Wang, Jianmin Wang, and Mingsheng Long · 2023
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Clipn for zero-shot ood detection: Teaching clip to say no
Hualiang Wang, Yi Li, Huifeng Yao, and Xiaomeng Li · 2023
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Negative label guided OOD detection with pretrained vision-language models
Xue Jiang, Feng Liu, Zhen Fang, Hong Chen, Tongliang Liu, Feng Zheng, and Bo Han · 2024
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