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Out-of-distribution (OOD) detection is indispensable for safely deploying machine learning models in the wild.
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
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Density level sets: Asymptotics, inference, and visualization
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On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger · 2017
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A baseline for detecting misclassified and out-of-distribution examples in neural networks
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Simple and scalable predictive uncertainty estimation using deep ensembles
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Places: A 10 million image database for scene recognition
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Discriminative out-of-distribution detection for semantic segmentation
Petra Bevandić, Ivan Krešo, Marin Oršić, and Siniša Šegvić · 2018
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Enhancing the reliability of out-of-distribution image detection in neural networks
Shiyu Liang, Yixuan Li, and Rayadurgam Srikant · 2018
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Predictive uncertainty estimation via prior networks
Andrey Malinin and Mark Gales · 2018
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The inaturalist species classification and detection dataset
Csi: Novelty detection via contrastive learning on distributionally shifted instances
Jihoon Tack, Sangwoo Mo, Jongheon Jeong, and Jinwoo Shin · 2020
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Uncertainty estimation using a single deep deterministic neural network
Joost van Amersfoort, Lewis Smith, Yee Whye Teh, and Yarin Gal · 2020
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Batchensemble: an alternative approach to efficient ensemble and lifelong learning
Yeming Wen, Dustin Tran, and Jimmy Ba · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 2021
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Exploring the limits of out-of-distribution detection
Stanislav Fort, Jie Ren, and Balaji Lakshminarayanan · 2021
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Selectivenet: A deep neural network with an integrated reject option
Yonatan Geifman and Ran El-Yaniv · 2019
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Why relu networks yield high-confidence predictions far away from the training data and how to mitigate the problem
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Deep anomaly detection with outlier exposure
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A simple baseline for bayesian uncertainty in deep learning
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Reverse kl-divergence training of prior networks: Improved uncertainty and adversarial robustness
Andrey Malinin and Mark Gales · 2019
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Directional statistics-based deep metric learning for image classification and retrieval
Xuefei Zhe, Shifeng Chen, and Hong Yan · 2019
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Mos: Towards scaling out-of-distribution detection for large semantic space
Rui Huang and Yixuan Li · 2021
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On the importance of gradients for detecting distributional shifts in the wild
Rui Huang, Andrew Geng, and Yixuan Li · 2021
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Mopro: Webly supervised learning with momentum prototypes
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Learning transferable visual models from natural language supervision
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Ssd: A unified framework for self-supervised outlier detection
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Can multi-label classification networks know what they don’t know?
Haoran Wang, Weitang Liu, Alex Bocchieri, and Yixuan Li · 2021
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Semantically coherent out-of-distribution detection
Jingkang Yang, Haoqi Wang, Litong Feng, Xiaopeng Yan, Huabin Zheng, Wayne Zhang, and Ziwei Liu · 2021
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Training OOD detectors in their natural habitats
Julian Katz-Samuels, Julia B. Nakhleh, Robert D. Nowak, and Yixuan Li · 2022
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Out-of-distribution detection with deep nearest neighbors
Yiyou Sun, Yifei Ming, Xiaojin Zhu, and Yixuan Li · 2022
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Vim: Out-of-distribution with virtual-logit matching
Haoqi Wang, Zhizhong Li, Litong Feng, and Wayne Zhang · 2022
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Mitigating neural network overconfidence with logit normalization
Hongxin Wei, Renchunzi Xie, Hao Cheng, Lei Feng, Bo An, and Yixuan Li · 2022
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How to exploit hyperspherical embeddings for out-of-distribution detection?
Yifei Ming, Yiyou Sun, Ousmane Dia, and Yixuan Li · 2023
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