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One critical challenge in deploying highly performant machine learning models in real-life applications is out of distribution (OOD) detection.
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He, K., Zhang, X., Ren, S., and Sun, J · 2016
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A baseline for detecting misclassified and out-of-distribution examples in neural networks
Hendrycks, D. and Gimpel, K · 2016
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Simple and scalable predictive uncertainty estimation using deep ensembles
Lakshminarayanan, B., Pritzel, A., and Blundell, C · 2016
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On calibration of modern neural networks
Guo, C., Pleiss, G., Sun, Y., and Weinberger, K. Q · 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
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Enhancing the reliability of out-of-distribution image detection in neural networks
Liang, S., Li, Y., and Srikant, R · 2017
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The inaturalist species classification and detection dataset-supplementary material
Van Horn, G., Mac Aodha, O., Song, Y., Cui, Y., Sun, C., Shepard, A., Adam, H., Perona, P., and Belongie, S · 2017
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Places: A 10 million image database for scene recognition
Zhou, B., Lapedriza, A., Khosla, A., Oliva, A., and Torralba, A · 2017
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Deep anomaly detection with outlier exposure
Hendrycks, D., Mazeika, M., and Dietterich, T · 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
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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
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Likelihood ratios for out-of-distribution detection
Ren, J., Liu, P. J., Fertig, E., Snoek, J., Poplin, R., DePristo, M. A., Dillon, J. V., and Lakshminarayanan, B · 2019
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Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
Rudin, C · 2019
Hyperparameter-free out-of-distribution detection using cosine similarity
Techapanurak, E., Suganuma, M., and Okatani, T · 2020
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Uncertainty estimation using a single deep deterministic neural network
Van Amersfoort, J., Smith, L., Teh, Y. W., and Gal, Y · 2020
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Mortality risk score for critically ill patients with viral or unspecified pneumonia: Assisting clinicians with covid-19 ecmo planning
Zhou, H., Cheng, C., Lipton, Z. C., Chen, G. H., and Weiss, J. C · 2020
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Deep residual flow for out of distribution detection
Zisselman, E. and Tamar, A · 2020
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Learning urban driving policies using deep reinforcement learning
Agarwal, T., Arora, H., and Schneider, J · 2021
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Input complexity and out-of-distribution detection with likelihood-based generative models
Serrà, J., Álvarez, D., Gómez, V., Slizovskaia, O., Núñez, J. F., and Luque, J · 2019
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Out-of-distribution detection in classifiers via generation
Vernekar, S., Gaurav, A., Abdelzad, V., Denouden, T., Salay, R., and Czarnecki, K · 2019
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Unsupervised out-of-distribution detection by maximum classifier discrepancy
Yu, Q. and Aizawa, K · 2019
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Estimating example difficulty using variance of gradients
Agarwal, C., D’souza, D., and Hooker, S · 2020
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Big transfer (bit): General visual representation learning
Kolesnikov, A., Beyer, L., Zhai, X., Puigcerver, J., Yung, J., Gelly, S., and Houlsby, N · 2020
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Gradients as a measure of uncertainty in neural networks
Lee, J. and AlRegib, G · 2020
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Efficient out-of-distribution detection in digital pathology using multi-head convolutional neural networks
Linmans, J., van der Laak, J., and Litjens, G · 2020
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Machine learning for tokamak scenario optimization: combining accelerating physics models and empirical models
Boyer, M., Wai, J., Clement, M., Kolemen, E., Char, I., Chung, Y., Neiswanger, W., and Schneider, J · 2021
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A model-based reinforcement learning approach for beta control
Char, I., Chung, Y., Boyer, M., Kolemen, E., and Schneider, J · 2021
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Mos: Towards scaling out-of-distribution detection for large semantic space
Huang, R. and Li, Y · 2021
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On the importance of gradients for detecting distributional shifts in the wild
Huang, R., Geng, A., and Li, Y · 2021
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On pitfalls in ood detection: Predictive entropy considered harmful
Kirsch, A., Mukhoti, J., Amersfoort, J. v., Torr, P. H., and Gal, Y · 2021
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Salehi, M., Mirzaei, H., Hendrycks, D., Li, Y., Rohban, M. H., and Sabokrou, M · 2021
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Rethinking calibration of deep neural networks: Do not be afraid of overconfidence
Wang, D.-B., Feng, L., and Zhang, M.-L · 2021
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Generalized out-of-distribution detection: A survey
Yang, J., Zhou, K., Li, Y., and Liu, Z · 2021
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