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Research on Out-Of-Distribution (OOD) detection focuses mainly on building scores that efficiently distinguish OOD data from In Distribution (ID) data.
Calibration of ρ \rho values for testing precise null hypotheses
Sellke, T. M., Bayarri, M. J., and Berger, J. O · 2001
Earlier work this paper cites.
Inductive confidence machines for regression
Papadopoulos, H., Proedrou, K., Vovk, V., and Gammerman, A · 2002
Earlier work this paper cites.
Testing exchangeability on-line
Vovk, V., Nouretdinov, I., and Gammerman, A · 2003
Earlier work this paper cites.
Algorithmic learning in a random world , volume 29
Vovk, V., Gammerman, A., and Shafer, G · 2005
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A tutorial on conformal prediction
Shafer, G. and Vovk, V · 2008
Earlier work this paper cites.
Energy-based Out-of-distribution Detection
Liu, W., Wang, X., Owens, J. D., and Li, Y · 2010
Earlier work this paper cites.
Sequential conformal anomaly detection in trajectories based on hausdorff distance
Laxhammar, R. and Falkman, G · 2011
Earlier work this paper cites.
Conditional Validity of Inductive Conformal Predictors
Vovk, V · 2012
Earlier work this paper cites.
Conformal prediction for reliable machine learning: theory, adaptations and applications
Balasubramanian, V., Ho, S.-S., and Vovk, V · 2014
Earlier work this paper cites.
Conformal anomaly detection
Laxhammar, R · 2014
Earlier work this paper cites.
Towards Open Set Deep Networks
Bendale, A. and Boult, T. E · 2015
Earlier work this paper cites.
A Baseline for Detecting Misclassified and Out-of-distribution Examples in Neural Networks
Hendrycks, D. and Gimpel, K · 2016
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Selective classification for deep neural networks
Geifman, Y. and El-Yaniv, R · 2017
Earlier work this paper cites.
On Calibration of Modern Neural Networks
Guo, C., Pleiss, G., Sun, Y., and Weinberger, K. Q · 2017
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Hendrycks, D. and Gimpel, K · 2018
Cited alongside, same era.
A Simple Unified Framework for Detecting Out-of-distribution Samples and Adversarial Attacks
Lee, K., Lee, K., Lee, H., and Shin, J · 2018
Cited alongside, same era.
Detecting multivariate outliers: Use a robust variant of the Mahalanobis distance
Leys, C., Klein, O., Dominicy, Y., and Ley, C · 2018
Cited alongside, same era.
Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks
Liang, S., Li, Y., and Srikant, R · 2018
Cited alongside, same era.
SelectiveNet: A deep neural network with an integrated reject option
Geifman, Y. and El-Yaniv, R · 2019
Cited alongside, same era.
Testing for Outliers with Conformal p-values, May 2022
Bates, S., Candès, E., Lei, L., Romano, Y., and Sesia, M · 2022
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Extremely Simple Activation Shaping for Out-of-distribution Detection
Djurisic, A., Bozanic, N., Ashok, A., and Liu, R · 2022
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Prediction and outlier detection in classification problems
Guan, L. and Tibshirani, R · 2022
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Adbench: Anomaly detection benchmark
Han, S., Hu, X., Huang, H., Jiang, M., and Zhao, Y · 2022
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Haroush, M., Frostig, T., Heller, R., and Soudry, D · 2022
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Least ambiguous set-valued classifiers with bounded error levels
Sadinle, M., Lei, J., and Wasserman, L · 2019
Cited alongside, same era.
Uncertainty sets for image classifiers using conformal prediction
Angelopoulos, A., Bates, S., Malik, J., and Jordan, M. I · 2020
Cited alongside, same era.
Classification with valid and adaptive coverage
Romano, Y., Sesia, M., and Candes, E · 2020
Cited alongside, same era.
Detecting Out-of-distribution Examples with Gram Matrices
Sastry, C. S. and Oore, S · 2020
Cited alongside, same era.
On the Importance of Gradients for Detecting Distributional Shifts in the Wild
Huang, R., Geng, A., and Li, Y · 2021
Cited alongside, same era.
Understanding softmax confidence and uncertainty
Pearce, T., Brintrup, A., and Zhu, J · 2021
Cited alongside, same era.
ReAct: Out-of-distribution Detection With Rectified Activations
Sun, Y., Guo, C., and Li, Y · 2021
Cited alongside, same era.
Scaling Out-of-distribution Detection for Real-world Settings
Hendrycks, D., Basart, S., Mazeika, M., Zou, A., Kwon, J., Mostajabi, M., Steinhardt, J., and Song, D · 2022
Later among the works it cites.
iDECODe: In-Distribution Equivariance for Conformal Out-of-Distribution Detection
Kaur, R., Jha, S., Roy, A., Park, S., Dobriban, E., Sokolsky, O., and Lee, I · 2022
Later among the works it cites.
Liang, Z., Sesia, M., and Sun, W · 2022
Later among the works it cites.
RankFeat: Rank-1 Feature Removal for Out-of-distribution Detection
Song, Y., Sebe, N., and Wang, W · 2022
Later among the works it cites.
DICE: Leveraging Sparsification for Out-of-distribution Detection
Sun, Y. and Li, Y · 2022
Later among the works it cites.
Out-of-distribution Detection with Deep Nearest Neighbors
Sun, Y., Ming, Y., Zhu, X., and Li, Y · 2022
Later among the works it cites.
ViM: Out-Of-distribution with Virtual-logit Matching
Wang, H., Li, Z., Feng, L., and Zhang, W · 2022
Later among the works it cites.
Openood: Benchmarking generalized out-of-distribution detection
Yang, J., Wang, P., Zou, D., Zhou, Z., Ding, K., Peng, W., Wang, H., Chen, G., Li, B., Sun, Y., et al · 2022
Later among the works it cites.
Out-of-distribution Detection based on In-distribution Data Patterns Memorization with Modern Hopfield Energy
Zhang, J., Fu, Q., Chen, X., Du, L., Li, Z., Wang, G., Liu, X., Han, S., and Zhang, D · 2023
Later among the works it cites.