Charles H Martin and Michael W Mahoney. Heavy-tailed universality predicts trends in test accuracies for very large pre-trained deep neural networks. In Proceedings of the 2020 SIAM International Conference on Data Mining, pages 505-513. SIAM, 2020.
Yifei Ming, Yiyou Sun, Ousmane Dia, and Yixuan Li. How to exploit hyperspherical embeddings for out-of-distribution detection. In The Eleventh International Conference on Learning Representations (ICLR), 2023.
Behnam Neyshabur, Srinadh Bhojanapalli, David McAllester, and Nati Srebro. Exploring generalization in deep learning. Advances in Neural Information Processing Systems, 30, 2017.
Sinno Jialin Pan, Ivor W Tsang, James T Kwok, and Qiang Yang. Domain adaptation via transfer component analysis. IEEE Transactions on Neural Networks, 22(2):199-210, 2010.
Emmanouil Platanios, Hoifung Poon, Tom M Mitchell, and Eric J Horvitz. Estimating accuracy from unlabeled data: A probabilistic logic approach. Advances in Neural Information Processing Systems, 30, 2017.
Emmanouil Antonios Platanios, Avinava Dubey, and Tom Mitchell. Estimating accuracy from unlabeled data: A bayesian approach. In International Conference on Machine Learning, pages 1416-1425. PMLR, 2016.
Joaquin Quinonero-Candela, Masashi Sugiyama, Anton Schwaighofer, and Neil D Lawrence. Dataset shift in machine learning. Mit Press, 2008.
Kate Saenko, Brian Kulis, Mario Fritz, and Trevor Darrell. Adapting visual category models to new domains. In European Conference on Computer Vision (ECCV), pages 213-226. Springer, 2010.
Aman Sinha, Hongseok Namkoong, Riccardo Volpi, and John Duchi. Certifying some distributional robustness with principled adversarial training. arXiv preprint arXiv:1710.10571, 2017.
Kagan Tumer and Joydeep Ghosh. Bayes error rate estimation using classifier ensembles. International Journal of Smart Engineering System Design, 5(2):95-109, 2003.
Eric Tzeng, Judy Hoffman, Ning Zhang, Kate Saenko, and Trevor Darrell. Deep domain confusion: Maximizing for domain invariance. arXiv preprint arXiv:1412.3474, 2014.
Eric Tzeng, Judy Hoffman, Kate Saenko, and Trevor Darrell. Adversarial discriminative domain adaptation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 7167-7176, 2017.
Thomas Unterthiner, Daniel Keysers, Sylvain Gelly, Olivier Bousquet, and Ilya Tolstikhin. Predicting neural network accuracy from weights. arXiv preprint arXiv:2002.11448, 2020.
Hemanth Venkateswara, Jose Eusebio, Shayok Chakraborty, and Sethuraman Panchanathan. Deep hashing network for unsupervised domain adaptation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 5018-5027, 2017.
Zijian Wang, Yadan Luo, Ruihong Qiu, Zi Huang, and Mahsa Baktashmotlagh. Learning to diversify for single domain generalization. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 834-843, 2021.
Andrew R Webb and Keith D Copsey. Introduction to statistical pattern recognition. Statistical Pattern Recognition, pages 1-31, 1990.
Hongxin Wei, Renchunzi Xie, Hao Cheng, Lei Feng, Bo An, and Yixuan Li. Mitigating neural network overconfidence with logit normalization. arXiv preprint arXiv:2205.09310, 2022.
Scott Yak, Javier Gonzalvo, and Hanna Mazzawi. Towards task and architecture-independent generalization gap predictors. arXiv preprint arXiv:1906.01550, 2019.
Tzay Y Young and Thomas W Calvert. Classification, estimation, and pattern recognition. Elsevier Publishing Company, 1974.