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Reliable detection of out-of-distribution (OOD) inputs is increasingly understood to be a precondition for deployment of machine learning systems.
Learning multiple layers of features from tiny images
Krizhevsky, A · 2009
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Reading digits in natural images with unsupervised feature learning
Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., and Ng, A. Y · 2011
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Toward open set recognition
Scheirer, W. J., de Rezende Rocha, A., Sapkota, A., and Boult, T. E · 2012
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Weight uncertainty in neural networks
Blundell, C., Cornebise, J., Kavukcuoglu, K., and Wierstra, D · 2015
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Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
Nguyen, A., Yosinski, J., and Clune, J · 2015
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Gal, Y. and Ghahramani, Z · 2016
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Deep residual learning for image recognition
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 · 2017
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Simple and scalable predictive uncertainty estimation using deep ensembles
Lakshminarayanan, B., Pritzel, A., and Blundell, C · 2017
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Sgdr: Stochastic gradient descent with warm restarts
Loshchilov, I. and Hutter, F · 2017
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Large batch training of convolutional networks
You, Y., Gitman, I., and Ginsburg, B · 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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A variational dirichlet framework for out-of-distribution detection
Chen, W., Shen, Y., Jin, H., and Wang, W · 2018
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Waic, but why? generative ensembles for robust anomaly detection
Choi, H., Jang, E., and Alemi, A. A · 2018
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Learning confidence for out-of-distribution detection in neural networks
DeVries, T. and Taylor, G. W · 2018
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Enhancing the reliability of out-of-distribution image detection in neural networks
Liang, S., Li, Y., and Srikant, R · 2018
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When does label smoothing help?
Müller, R., Kornblith, S., and Hinton, G. E · 2019
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Do imagenet classifiers generalize to imagenet?
Recht, B., Roelofs, R., Schmidt, L., and Shankar, V · 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., Dillon, J., and Lakshminarayanan, B · 2019
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A simple framework for contrastive learning of visual representations
Chen, T., Kornblith, S., Norouzi, M., and Hinton, G · 2020
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Data-efficient image recognition with contrastive predictive coding
Hénaff, O. J., Srinivas, A., Fauw, J. D., Razavi, A., Doersch, C., Eslami, S. M. A., and van den Oord, A · 2020
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Why is the mahalanobis distance effective for anomaly detection?
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Predictive uncertainty estimation via prior networks
Malinin, A. and Gales, M · 2018
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Metric learning for novelty and anomaly detection
Masana, M., Ruiz, I., Serrat, J., van de Weijer, J., and Lopez, A. M · 2018
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Out-of-distribution detection using multiple semantic label representations
Shalev, G., Adi, Y., and Keshet, J · 2018
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Out-of-distribution detection using an ensemble of self supervised leave-out classifiers
Vyas, A., Jammalamadaka, N., Zhu, X., Das, D., Kaul, B., and Willke, T. L · 2018
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Learning representations by maximizing mutual information across views
Bachman, P., Hjelm, R. D., and Buchwalter, W · 2019
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Momentum contrast for unsupervised visual representation learning
He, K., Fan, H., Wu, Y., Xie, S., and Girshick, R · 2019
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Deep anomaly detection with outlier exposure
Hendrycks, D., Mazeika, M., and Dietterich, T
Cited in the paper.
Kamoi, R. and Kobayashi, K · 2020
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Self-supervised learning for generalizable out-of-distribution detection
Mohseni, S., Pitale, M., Yadawa, J., and Wang, Z · 2020
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Detecting out-of-distribution examples with gram matrices
Sastry, C. S. and Oore, S · 2020
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Hybrid models for open set recognition
Zhang, H., Li, A., Guo, J., and Guo, Y · 2020
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Deep residual flow for novelty detection
Zisselman, E. and Tamar, A · 2020
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