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Out-of-distribution (OOD) detection approaches usually present special requirements (e.g., hyperparameter validation, collection of outlier data) and produce side effects (e.g., classification accuracy drop, slower energy-inefficient inferences).
E. T. Jaynes, “Information theory and statistical mechanics,” Physical Review
1957
Earlier work this paper cites.
E. T. Jaynes, “Information theory and statistical mechanics. II,” Physical Review
1957
Earlier work this paper cites.
T. M. Cover and J. A. Thomas, “Elements of information theory,” Wiley Series in Telecommunications and Signal Processing
2006
Earlier work this paper cites.
J. Deng, W. Dong, R. Socher, L. Li, K. Li, and F. Li, “ImageNet: A large-scale hierarchical image database,” IEEE International Conference on Computer Vision and Pattern Recognition
2009
Earlier work this paper cites.
A. Krizhevsky, “Learning multiple layers of features from tiny images,” Science Department, University of Toronto
2009
Earlier work this paper cites.
X. Glorot and Y. Bengio, “Understanding the difficulty of training deep feedforward neural networks,” International Conference on Artificial Intelligence and Statistics
2010
Earlier work this paper cites.
Y. Netzer and T. Wang, “Reading digits in natural images with unsupervised feature learning,” Neural Information Processing Systems
2011
Earlier work this paper cites.
T. Mensink, J. J. Verbeek, F. Perronnin, and G. Csurka, “Distance-based image classification: Generalizing to new classes at near-zero cost,” IEEE Transactions on Pattern Analysis and Machine Intelligence
2013
Earlier work this paper cites.
W. J. Scheirer, A. Rocha, A. Sapkota, and T. E. Boult, “Towards open set recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence
2013
Earlier work this paper cites.
W. J. Scheirer, L. P. Jain, and T. E. Boult, “Probability models for open set recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence
2014
Earlier work this paper cites.
A. Bendale and T. Boult, “Towards open world recognition,” IEEE International Conference on Computer Vision and Pattern Recognition
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
Y. Wen, K. Zhang, Z. Li, and Y. Qiao, “A discriminative feature learning approach for deep face recognition,” European Conference on Computer Vision
2016
Earlier work this paper cites.
W. Liu, Y. Wen, Z. Yu, and M. Yang, “Large-margin softmax loss for convolutional neural networks.,” International Conference on Machine Learning
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Delving deep into rectifiers: Surpassing human-level performance on imagenet classification,” International Conference on Computer Vision
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Identity mappings in deep residual networks,” European Conference on Computer Vision
2016
Earlier work this paper cites.
A. Kendall and Y. Gal, “What uncertainties do we need in bayesian deep learning for computer vision?,” Neural Information Processing Systems
2017
Cited alongside, same era.
C. Leibig, V. Allken, M. S. Ayhan, P. Berens, and S. Wahl, “Leveraging uncertainty information from deep neural networks for disease detection,” Scientific Reports
2017
Cited alongside, same era.
2017
Cited alongside, same era.
C. Guo, G. Pleiss, Y. Sun, and K. Q. Weinberger, “On calibration of modern neural networks,” International Conference on Machine Learning
2017
Cited alongside, same era.
D. Hendrycks and K. Gimpel, “A baseline for detecting misclassified and out-of-distribution examples in neural networks,” International Conference on Learning Representations
D. Hendrycks, M. Mazeika, and T. Dietterich, “Deep anomaly detection with outlier exposure,” International Conference on Learning Representations
2019
Closest in time.
2019
Closest in time.
A. Shafaei, M. Schmidt, and J. J. Little, “A less biased evaluation of out-of-distribution sample detectors,” British Machine Vision Conference
2019
Closest in time.
J. Ren, P. J. Liu, E. Fertig, J. Snoek, R. Poplin, M. A. DePristo, J. V. Dillon, and B. Lakshminarayanan, “Likelihood ratios for out-of-distribution detection,” Neural Information Processing Systems
2019
Closest in time.
J. Deng, J. Guo, N. Xue, and S. Zafeiriou, “ArcFace: Additive angular margin loss for deep face recognition,” IEEE International Conference on Computer Vision and Pattern Recognition
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2017
Cited alongside, same era.
G. Huang, Z. Liu, L. v. d. Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” IEEE International Conference on Computer Vision and Pattern Recognition
2017
Cited alongside, same era.
K. Lee, K. Lee, H. Lee, and J. Shin, “A simple unified framework for detecting out-of-distribution samples and adversarial attacks,” Neural Information Processing Systems
2018
Cited alongside, same era.
E. Rudd, L. P. Jain, W. J. Scheirer, and T. Boult, “The extreme value machine,” IEEE Transactions on Pattern Analysis and Machine Intelligence
2018
Cited alongside, same era.
2018
Cited alongside, same era.
M. Hein, M. Andriushchenko, and J. Bitterwolf, “Why ReLU networks yield high-confidence predictions far away from the training data and how to mitigate the problem,” IEEE International Conference on Computer Vision and Pattern Recognition
2018
Cited alongside, same era.
S. Liang, Y. Li, and R. Srikant, “Enhancing the reliability of out-of-distribution image detection in neural networks,” International Conference on Learning Representations
2018
Cited alongside, same era.
A. R. Dhamija, M. Günther, and T. Boult, “Reducing network agnostophobia,” Neural Information Processing Systems
2018
Cited alongside, same era.
2019
Closest in time.
D. Tsipras, S. Santurkar, L. Engstrom, A. Turner, and A. Madry, “Robustness may be at odds with accuracy,” 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019
2019
Closest in time.
2019
Closest in time.
R. Schwartz, J. Dodge, N. A. Smith, and O. Etzioni, “Green AI,” CoRR
2019
Closest in time.
A. Shafahi, M. Najibi, A. Ghiasi, Z. Xu, J. P. Dickerson, C. Studer, L. S. Davis, G. Taylor, and T. Goldstein, “Adversarial training for free!,” Neural Information Processing Systems
2019
Closest in time.
S. Thulasidasan, G. Chennupati, J. A. Bilmes, T. Bhattacharya, and S. Michalak, “On mixup training: Improved calibration and predictive uncertainty for deep neural networks,” Neural Information Processing Systems
2019
Closest in time.
S. Yun, D. Han, S. J. Oh, S. Chun, J. Choe, and Y. Yoo, “Cutmix: Regularization strategy to train strong classifiers with localizable features,” International Conference on Computer Vision
2019
Closest in time.
Y.-C. Hsu, Y. Shen, H. Jin, and Z. Kira, “Generalized ODIN: Detecting out-of-distribution image without learning from out-of-distribution data,” IEEE International Conference on Computer Vision and Pattern Recognition
2020
Closest in time.
W. Liu, X. Wang, J. D. Owens, and Y. Li, “Energy-based out-of-distribution detection,” CoRR
2020
Closest in time.
E. Techapanurak, M. Suganuma, and T. Okatani, “Hyperparameter-free out-of-distribution detection using cosine similarity,” Proceedings of the Asian Conference on Computer Vision (ACCV)
2020
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C. S. Sastry and S. Oore, “Detecting out-of-distribution examples with gram matrices,” International Conference on Machine Learning
2020
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