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Reliable application of machine learning-based decision systems in the wild is one of the major challenges currently investigated by the field.
An optimum character recognition system using decision functions
C. Chow · 1957
Earlier work this paper cites.
Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods
J. C. Platt · 1999
Earlier work this paper cites.
Strictly Proper Scoring Rules, Prediction, and Estimation
T. Gneiting and A. E. Raftery · 2007
Earlier work this paper cites.
Contrastive Training for Improved Out-of-Distribution Detection
J. Winkens, R. Bunel, A. G. Roy, R. Stanforth, V. Natarajan, J. R. Ledsam, P. MacWilliams, P. Kohli, A. Karthikesalingam, S. Kohl, T. Cemgil, S. M. A. Eslami, and O. Ronneberger · 2007
Earlier work this paper cites.
Learning Multiple Layers of Features from Tiny Images
A. Krizhevsky · 2009
Earlier work this paper cites.
Dataset Shift in Machine Learning
J. Quionero-Candela, M. Sugiyama, A. Schwaighofer, and N. Lawrence · 2009
Earlier work this paper cites.
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, and N. Houlsby · 2010
Earlier work this paper cites.
On the Foundations of Noise-free Selective Classification
R. El-Yaniv and Y. Wiener · 2010
Earlier work this paper cites.
Reading Digits in Natural Images with Unsupervised Feature Learning
Y. Netzer, T. Wang, A. Coates, A. Bissacco, B. Wu, and A. Y. Ng · 2011
Earlier work this paper cites.
WILDS: A Benchmark of in-the-Wild Distribution Shifts
P. W. Koh, S. Sagawa, H. Marklund, S. M. Xie, M. Zhang, A. Balsubramani, W. Hu, M. Yasunaga, R. L. Phillips, I. Gao, T. Lee, E. David, I. Stavness, W. Guo, B. A. Earnshaw, I. S. Haque, S. Beery, J. Leskovec, A. Kundaje, E. Pierson, S. Levine, C. Finn, and P. Liang · 2012
Earlier work this paper cites.
Post-hoc Uncertainty Calibration for Domain Drift Scenarios
C. Tomani, S. Gruber, M. E. Erdem, D. Cremers, and F. Buettner · 2012
Earlier work this paper cites.
Deep Residual Learning for Image Recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2015
Earlier work this paper cites.
Tiny ImageNet Visual Recognition Challenge
Y. Le and X. Yang · 2015
Earlier work this paper cites.
Very Deep Convolutional Networks for Large-Scale Image Recognition
K. Simonyan and A. Zisserman · 2015
Earlier work this paper cites.
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Y. Gal and Z. Ghahramani · 2016
Earlier work this paper cites.
Selective Classification for Deep Neural Networks
Y. Geifman and R. El-Yaniv · 2017
Earlier work this paper cites.
On Calibration of Modern Neural Networks
C. Guo, G. Pleiss, Y. Sun, and K. Q. Weinberger · 2017
Earlier work this paper cites.
A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks
D. Hendrycks and K. Gimpel · 2017
Cited alongside, same era.
What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?
A. Kendall and Y. Gal · 2017
Cited alongside, same era.
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
B. Lakshminarayanan, A. Pritzel, and C. Blundell · 2017
Cited alongside, same era.
SGDR: Stochastic Gradient Descent with Warm Restarts
I. Loshchilov and F. Hutter · 2017
Cited alongside, same era.
Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning
S. Depeweg, J. M. Hernández-Lobato, F. Doshi-Velez, and S. Udluft · 2018
Cited alongside, same era.
Deep Gamblers: Learning to Abstain with Portfolio Theory
Z. Liu, Z. Wang, P. P. Liang, R. R. Salakhutdinov, L.-P. Morency, and M. Ueda · 2019
Later among the works it cites.
Can You Trust Your Model’s Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift
Y. Ovadia, E. Fertig, J. Ren, Z. Nado, D. Sculley, S. Nowozin, J. V. Dillon, B. Lakshminarayanan, and J. Snoek · 2019
Later among the works it cites.
Detecting Semantic Anomalies
F. Ahmed and A. Courville · 2020
Later among the works it cites.
Shortcut learning in deep neural networks
R. Geirhos, J.-H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F. A. Wichmann · 2020
Later among the works it cites.
Selective Question Answering under Domain Shift
A. Kamath, R. Jia, and P. Liang · 2020
Later among the works it cites.
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T. DeVries and G. W. Taylor · 2018
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To Trust Or Not To Trust A Classifier
H. Jiang, B. Kim, M. Guan, and M. Gupta · 2018
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A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks
K. Lee, K. Lee, H. Lee, and J. Shin · 2018
Cited alongside, same era.
Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks
S. Liang, Y. Li, and R. Srikant · 2018
Cited alongside, same era.
Troubling Trends in Machine Learning Scholarship
Z. C. Lipton and J. Steinhardt · 2018
Cited alongside, same era.
Predictive Uncertainty Estimation via Prior Networks
A. Malinin and M. Gales · 2018
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Winner’s Curse? On Pace, Progress, and Empirical Rigor
D. Sculley, J. Snoek, A. Wiltschko, and A. Rahimi · 2018
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H. Zhang, A. Li, J. Guo, and Y. Guo · 2020
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Exploring the Limits of Out-of-Distribution Detection
S. Fort, J. Ren, and B. Lakshminarayanan · 2021
Later among the works it cites.
Common limitations of image processing metrics: A picture story
A. Reinke, M. Eisenmann, M. D. Tizabi, C. H. Sudre, T. Rädsch, M. Antonelli, T. Arbel, S. Bakas, M. J. Cardoso, V. Cheplygina, et al · 2021
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A Simple Fix to Mahalanobis Distance for Improving Near-OOD Detection
J. Ren, S. Fort, J. Liu, A. G. Roy, S. Padhy, and B. Lakshminarayanan · 2021
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A Unifying Review of Deep and Shallow Anomaly Detection
L. Ruff, J. R. Kauffmann, R. A. Vandermeulen, G. Montavon, W. Samek, M. Kloft, T. G. Dietterich, and K.-R. Müller · 2021
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BREEDS: Benchmarks for Subpopulation Shift
S. Santurkar, D. Tsipras, and A. Madry · 2021
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Failure detection in medical image classification: A reality check and benchmarking testbed
M. Bernhardt, F. D. S. Ribeiro, and B. Glocker · 2022
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W. Liang and J. Zou · 2022
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Plex: Towards reliability using pretrained large model extensions
D. Tran, J. Liu, M. W. Dusenberry, D. Phan, M. Collier, J. Ren, K. Han, Z. Wang, Z. Mariet, H. Hu, et al · 2022
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Open-Set Recognition: a Good Closed-Set Classifier is All You Need?
S. Vaze, K. Han, A. Vedaldi, and A. Zisserman · 2022
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A FINE-GRAINED ANALYSIS ON DISTRIBUTION SHIFT
O. Wiles, S. Gowal, F. Stimberg, S.-A. Rebuffi, I. Ktena, and T. Cemgil · 2022
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