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Uncertainty estimation is an essential step in the evaluation of the robustness for deep learning models in computer vision, especially when applied in risk-sensitive areas.
Deeper connections between neural networks and Gaussian processes speed-up active learning
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Sampling-free Epistemic Uncertainty Estimation Using Approximated Variance Propagation
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Estimating the mean and variance of the target probability distribution
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Pitfalls of in-domain uncertainty estimation and ensembling in deep learning
Ashukha, A.; Lyzhov, A.; Molchanov, D.; and Vetrov, D. 2020 · 2002
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CRUDE: Calibrating Regression Uncertainty Distributions Empirically
Zelikman, E.; Healy, C.; Zhou, S.; and Avati, A. 2020 · 2005
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Imagenet: A large-scale hierarchical image database
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Practical Variational Inference for Neural Networks
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Bayesian learning via stochastic gradient Langevin dynamics
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Imagenet classification with deep convolutional neural networks
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Reshaping deep neural network for fast decoding by node-pruning
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Conditional generative adversarial nets
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Bayesian dark knowledge
Balan, A. K.; Rathod, V.; Murphy, K. P.; and Welling, M. 2015 · 2015
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Deepdriving: Learning affordance for direct perception in autonomous driving
Chen, C.; Seff, A.; Kornhauser, A.; and Xiao, J. 2015 · 2015
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Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding
Han, S.; Mao, H.; and Dally, W. J. 2015 · 2015
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2015 · 2015
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Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks
Hernández-Lobato, J. M.; and Adams, R. 2015 · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S.; and Szegedy, C. 2015 · 2015
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Kendall, A.; Badrinarayanan, V.; and Cipolla, R. 2015 · 2015
On calibration of modern neural networks
Guo, C.; Pleiss, G.; Sun, Y.; and Weinberger, K. Q. 2017 · 2017
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What uncertainties do we need in bayesian deep learning for computer vision?
Kendall, A.; and Gal, Y. 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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Photo-realistic single image super-resolution using a generative adversarial network
Ledig, C.; Theis, L.; Huszár, F.; Caballero, J.; Cunningham, A.; Acosta, A.; Aitken, A.; Tejani, A.; Totz, J.; Wang, Z.; et al. 2017 · 2017
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Unsupervised medical image segmentation based on the local center of mass
Aganj, I.; Harisinghani, M. G.; Weissleder, R.; and Fischl, B. 2018 · 2018
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U-net: Convolutional networks for biomedical image segmentation
Ronneberger, O.; Fischer, P.; and Brox, T. 2015 · 2015
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Group equivariant convolutional networks
Cohen, T.; and Welling, M. 2016 · 2016
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Gal, Y.; and Ghahramani, Z. 2016 · 2016
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Deep residual learning for image recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 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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Loss-aware binarization of deep networks
Hou, L.; Yao, Q.; and Kwok, J. T. 2016 · 2016
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Deeper depth prediction with fully convolutional residual networks
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Bahat, Y.; and Shakhnarovich, G. 2018 · 2018
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Efficient uncertainty estimation for semantic segmentation in videos
Huang, P.-Y.; Hsu, W.-T.; Chiu, C.-Y.; Wu, T.-F.; and Sun, M. 2018 · 2018
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Uncertainty Estimates and Multi-hypotheses Networks for Optical Flow
Ilg, E.; c · 2018
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Accurate uncertainties for deep learning using calibrated regression
Kuleshov, V.; Fenner, N.; and Ermon, S. 2018 · 2018
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Feed-forward Propagation in Probabilistic Neural Networks with Categorical and Max Layers
Shekhovtsov, A.; and Flach, B. 2018 · 2018
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Bayesian uncertainty estimation for batch normalized deep networks
Teye, M.; Azizpour, H.; and Smith, K. 2018 · 2018
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Distribution calibration for regression
Song, H.; Diethe, T.; Kull, M.; and Flach, P. 2019 · 2019
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Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks
Wang, G.; Li, W.; Aertsen, M.; Deprest, J.; Ourselin, S.; and Vercauteren, T. 2019 · 2019
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On the uncertainty of self-supervised monocular depth estimation
Poggi, M.; Aleotti, F.; Tosi, F.; and Mattoccia, S. 2020 · 2020
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Individual calibration with randomized forecasting
Zhao, S.; Ma, T.; and Ermon, S. 2020 · 2020
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Reducing Uncertainty in Undersampled MRI Reconstruction with Active Acquisition
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