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Machine learning models encounter Out-of-Distribution (OoD) errors when the data seen at test time are generated from a different stochastic generator than the one used to generate the training data.
Novelty detection and neural network validation
Bishop, C. M · 1994
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Novelty detection in learning systems
Marsland, S · 2003
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Algebraic geometry and statistical learning theory , volume 25
Watanabe, S · 2009
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Watanabe, S · 2010
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Intriguing properties of neural networks
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., and Fergus, R · 2013
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Nice: Non-linear independent components estimation
Dinh, L., Krueger, D., and Bengio, Y · 2014
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Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
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Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2014
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Stochastic backpropagation and approximate inference in deep generative models
Rezende, D. J., Mohamed, S., and Wierstra, D · 2014
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Calibrating probability with undersampling for unbalanced classification
Dal Pozzolo, A., Caelen, O., Johnson, R. A., and Bontempi, G · 2015
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Explaining and harnessing adversarial examples
Goodfellow, I. J., Shlens, J., and Szegedy, C · 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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Variational inference with normalizing flows
Rezende, D. J. and Mohamed, S · 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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Neural autoregressive distribution estimation
Uria, B., Côté, M.-A., Gregor, K., Murray, I., and Larochelle, H · 2016
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Arjovsky, M., Chintala, S., and Bottou, L · 2017
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Dillon, J. V., Langmore, I., Tran, D., Brevdo, E., Vasudevan, S., Moore, D., Patton, B., Alemi, A., Hoffman, M., and Saurous, R. A · 2017
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Anomaly detection with generative adversarial networks
Deecke, L., Vandermeulen, R., Ruff, L., Mandt, S., and Kloft, M · 2018
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Deep anomaly detection with outlier exposure
Hendrycks, D., Mazeika, M., and Dietterich, T. G · 2018
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Glow: Generative flow with invertible 1x1 convolutions
Kingma, D. P. and Dhariwal, P · 2018
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Kliger, M. and Fleishman, S · 2018
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Adversarial examples for generative models
Kos, J., Fischer, I., and Song, D · 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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Fu, J., Co-Reyes, J., and Levine, S · 2017
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beta-vae: Learning basic visual concepts with a constrained variational framework
Higgins, I., Matthey, L., Pal, A., Burgess, C., Glorot, X., Botvinick, M., Mohamed, S., and Lerchner, A · 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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Stochastic gradient descent as approximate bayesian inference
Mandt, S., Hoffman, M. D., and Blei, D. M · 2017
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Masked autoregressive flow for density estimation
Papamakarios, G., Murray, I., and Pavlakou, T · 2017
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Unsupervised anomaly detection with generative adversarial networks to guide marker discovery
Schlegl, T., Seeböck, P., Waldstein, S. M., Schmidt-Erfurth, U., and Langs, G · 2017
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
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Do deep generative models know what they don’t know?
Nalisnick, E., Matsukawa, A., Teh, Y. W., Gorur, D., and Lakshminarayanan, B · 2018
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Pixeldefend: Leveraging generative models to understand and defend against adversarial examples
Song, Y., Kim, T., Nowozin, S., Ermon, S., and Kushman, N · 2018
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Tensor2tensor for neural machine translation
Vaswani, A., Bengio, S., Brevdo, E., Chollet, F., Gomez, A. N., Gouws, S., Jones, L., Kaiser, L., Kalchbrenner, N., Parmar, N., Sepassi, R., Shazeer, N., and Uszkoreit, J · 2018
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High-resolution image synthesis and semantic manipulation with conditional gans
Wang, T.-C., Liu, M.-Y., Zhu, J.-Y., Tao, A., Kautz, J., and Catanzaro, B · 2018
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Benchmarking neural network robustness to common corruptions and perturbations
Hendrycks, D. and Dietterich, T · 2019
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