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The distribution of a neural network's latent representations has been successfully used to detect out-of-distribution (OOD) data.
Detecting out-of-distribution inputs to deep generative models using typicality
Nalisnick, E., Matsukawa, A., Teh, Y. W., and Lakshminarayanan, B · 1906
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A practical bayesian framework for backpropagation networks
MacKay, D. J · 1992
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Keeping the neural networks simple by minimizing the description length of the weights
Hinton, G. E. and Van Camp, D · 1993
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Mixture density networks
Bishop, C. M · 1994
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Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P · 1998
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Aleatory or epistemic? does it matter?
Kiureghian, A. D. and Ditlevsen, O · 2008
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Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
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An analysis of single-layer networks in unsupervised feature learning
Coates, A., Ng, A., and Lee, H · 2011
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Mcmc using hamiltonian dynamics
Neal, R. M. et al · 2011
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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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Are we ready for autonomous driving? the kitti vision benchmark suite
Geiger, A., Lenz, P., and Urtasun, R · 2012
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Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups
Hinton, G., Deng, L., Yu, D., Dahl, G. E., Mohamed, A.-r., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T. N., et al · 2012
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Bayesian learning for neural networks , volume 118
Neal, R. M · 2012
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Nice: Non-linear independent components estimation
Dinh, L., Krueger, D., and Bengio, Y · 2014
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
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A review of novelty detection
Pimentel, M. A., Clifton, D. A., Clifton, L., and Tarassenko, L · 2014
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Reliable classification: Learning classifiers that distinguish aleatoric and epistemic uncertainty
Senge, R., Bösner, S., Dembczyński, K., Haasenritter, J., Hirsch, O., Donner-Banzhoff, N., and Hüllermeier, E · 2014
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Variational dropout and the local reparameterization trick
Kingma, D. P., Salimans, T., and Welling, M · 2015
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Human-level concept learning through probabilistic program induction
Lake, B. M., Salakhutdinov, R., and Tenenbaum, J. B · 2015
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Deep variational information bottleneck
Alemi, A. A., Fischer, I., Dillon, J. V., and Murphy, K · 2016
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Concrete problems in ai safety
Amodei, D., Olah, C., Steinhardt, J., Christiano, P., Schulman, J., and Mané, D · 2016
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The cityscapes dataset for semantic urban scene understanding
Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., and Schiele, B · 2016
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Uncertainty in deep learning
Gal, Y · 2016
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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 · 2016
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Risk versus uncertainty in deep learning: Bayes, bootstrap and the dangers of dropout
Osband, I · 2016
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Lost and found: detecting small road hazards for self-driving vehicles
Pinggera, P., Ramos, S., Gehrig, S., Franke, U., Rother, C., and Mester, R · 2016
Encoder-decoder with atrous separable convolution for semantic image segmentation
Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H · 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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Towards principled uncertainty estimation for deep neural networks
Harang, R. and Rudd, E. M · 2018
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Glow: Generative flow with invertible 1x1 convolutions
Kingma, D. P. and Dhariwal, P · 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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You only look once: Unified, Real-Time object detection
Redmon, J., Divvala, S., Girshick, R., and Farhadi, A · 2016
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Density estimation using real nvp
Dinh, L., Sohl-Dickstein, J., and Bengio, S · 2017
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Deep bayesian active learning with image data
Gal, Y., Islam, R., and Ghahramani, Z · 2017
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Unsupervised monocular depth estimation with left-right consistency
Godard, C., Mac Aodha, O., and Brostow, G. J · 2017
Cited alongside, same era.
On calibration of modern neural networks
Guo, C., Pleiss, G., Sun, Y., and Weinberger, K. Q · 2017
Cited alongside, same era.
Computer vision for autonomous vehicles: Problems, datasets and state-of-the-art
Janai, J., Güney, F., Behl, A., and Geiger, A · 2017
Cited alongside, same era.
Li, D., Chen, D., Goh, J., and Ng, S.-K · 2018
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Predictive uncertainty estimation via prior networks
Malinin, A. and Gales, M · 2018
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Deep k-nearest neighbors: Towards confident, interpretable and robust deep learning
Papernot, N. and McDaniel, P · 2018
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Deep one-class classification
Ruff, L., Vandermeulen, R., Goernitz, N., Deecke, L., Siddiqui, S. A., Binder, A., Müller, E., and Kloft, M · 2018
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Are generative deep models for novelty detection truly better?
Škvára, V., Pevnỳ, T., and Šmídl, V · 2018
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Bayesian uncertainty estimation for batch normalized deep networks
Teye, M., Azizpour, H., and Smith, K · 2018
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Guided image generation with conditional invertible neural networks
Ardizzone, L., Lüth, C., Kruse, J., Rother, C., and Köthe, U · 2019
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The fishyscapes benchmark: Measuring blind spots in semantic segmentation
Blum, H., Sarlin, P.-E., Nieto, J., Siegwart, R., and Cadena, C · 2019
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Safety for mobile robotic systems: A systematic mapping study from a software engineering perspective
Bozhinoski, D., Di Ruscio, D., Malavolta, I., Pelliccione, P., and Crnkovic, I · 2019
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Deep learning for anomaly detection: A survey
Chalapathy, R. and Chawla, S · 2019
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Sampling-free epistemic uncertainty estimation using approximated variance propagation
Postels, J., Ferroni, F., Coskun, H., Navab, N., and Tombari, F · 2019
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Can you trust your model’s uncertainty? evaluating predictive uncertainty under dataset shift
Snoek, J., Ovadia, Y., Fertig, E., Lakshminarayanan, B., Nowozin, S., Sculley, D., Dillon, J., Ren, J., and Nado, Z · 2019
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Simple and principled uncertainty estimation with deterministic deep learning via distance awareness
Liu, J. Z., Lin, Z., Padhy, S., Tran, D., Bedrax-Weiss, T., and Lakshminarayanan, B · 2020
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Density of states estimation for out-of-distribution detection
Morningstar, W. R., Ham, C., Gallagher, A. G., Lakshminarayanan, B., Alemi, A. A., and Dillon, J. V · 2020
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van Amersfoort, J., Smith, L., Teh, Y. W., and Gal, Y · 2020
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How good is the bayes posterior in deep neural networks really?
Wenzel, F., Roth, K., Veeling, B. S., Światkowski, J., Tran, L., Mandt, S., Snoek, J., Salimans, T., Jenatton, R., and Nowozin, S · 2020
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