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Detecting out-of-distribution (OOD) inputs is critical for safely deploying deep learning models in the real world.
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Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition
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Evasion attacks against machine learning at test time
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Intriguing properties of neural networks
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Goodfellow, I. J.; Shlens, J.; and Szegedy, C. 2014 · 2014
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Turkergaze: Crowdsourcing saliency with webcam based eye tracking
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Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop
Yu, F.; Seff, A.; Zhang, Y.; Song, S.; Funkhouser, T.; and Xiao, J. 2015 · 2015
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Towards Open Set Deep Networks
Bendale, A.; and Boult, T. E. 2016 · 2016
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A baseline for detecting misclassified and out-of-distribution examples in neural networks
Hendrycks, D.; and Gimpel, K. 2016 · 2016
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Sgdr: Stochastic gradient descent with warm restarts
Loshchilov, I.; and Hutter, F. 2016 · 2016
Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
Athalye, A.; Carlini, N.; and Wagner, D. 2018 · 2018
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Discriminative out-of-distribution detection for semantic segmentation
Bevandić, P.; Krešo, I.; Oršić, M.; and Šegvić, S. 2018 · 2018
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Deep anomaly detection with outlier exposure
Hendrycks, D.; Mazeika, M.; and Dietterich, T. G. 2018 · 2018
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A simple unified framework for detecting out-of-distribution samples and adversarial attacks
Lee, K.; Lee, K.; Lee, H.; and Shin, J. 2018 · 2018
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Predictive uncertainty estimation via prior networks
Malinin, A.; and Gales, M. 2018 · 2018
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The limitations of deep learning in adversarial settings
Papernot, N.; McDaniel, P.; Jha, S.; Fredrikson, M.; Celik, Z. B.; and Swami, A. 2016 · 2016
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On calibration of modern neural networks
Guo, C.; Pleiss, G.; Sun, Y.; and Weinberger, K. Q. 2017 · 2017
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Densely connected convolutional networks
Huang, G.; Liu, Z.; Van Der Maaten, L.; and Weinberger, K. Q. 2017 · 2017
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Simple and scalable predictive uncertainty estimation using deep ensembles
Lakshminarayanan, B.; Pritzel, A.; and Blundell, C. 2017 · 2017
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Training confidence-calibrated classifiers for detecting out-of-distribution samples
Lee, K.; Lee, H.; Lee, K.; and Shin, J. 2017 · 2017
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Enhancing the reliability of out-of-distribution image detection in neural networks
Liang, S.; Li, Y.; and Srikant, R. 2017 · 2017
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Towards deep learning models resistant to adversarial attacks
Madry, A.; Makelov, A.; Schmidt, L.; Tsipras, D.; and Vladu, A. 2017 · 2017
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Why ReLU networks yield high-confidence predictions far away from the training data and how to mitigate the problem
Hein, M.; Andriushchenko, M.; and Bitterwolf, J. 2019 · 2019
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Analyzing the robustness of open-world machine learning
Sehwag, V.; Bhagoji, A. N.; Song, L.; Sitawarin, C.; Cullina, D.; Chiang, M.; and Mittal, P. 2019 · 2019
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Certifiably Adversarially Robust Detection of Out-of-Distribution Data
Bitterwolf, J.; Meinke, A.; and Hein, M. 2020 · 2020
Closest in time.
Generalized ODIN: Detecting Out-of-Distribution Image Without Learning From Out-of-Distribution Data
Hsu, Y.; Shen, Y.; Jin, H.; and Kira, Z. 2020 · 2020
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Energy-based Out-of-distribution Detection
Liu, W.; Wang, X.; Owens, J. D.; and Li, Y. 2020 · 2020
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Self-Supervised Learning for Generalizable Out-of-Distribution Detection
Mohseni, S.; Pitale, M.; Yadawa, J.; and Wang, Z. 2020 · 2020
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ATOM: Robustifying Out-of-Distribution Detection Using Outlier Mining
Chen, J.; Li, Y.; Wu, X.; Liang, Y.; and Jha, S. 2021 · 2021
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MOS: Towards Scaling Out-of-Distribution Detection for Large Semantic Space
Huang, R.; and Li, Y. 2021 · 2021
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MOOD: Multi-Level Out-of-Distribution Detection
Lin, Z.; Roy, S. D.; and Li, Y. 2021 · 2021
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