[1] Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz. Invariant risk minimization. arXiv preprint arXiv:1907.02893, 2019.
[2] David Berthelot, Nicholas Carlini, Ian Goodfellow, Nicolas Papernot, Avital Oliver, and Colin A Raffel. Mixmatch: A holistic approach to semi-supervised learning. In Advances in Neural Information Processing Systems, 2019.
[3] Malik Boudiaf, Jérôme Rony, Imtiaz Masud Ziko, Eric Granger, Marco Pedersoli, Pablo Piantanida, and Ismail Ben Ayed. A unifying mutual information view of metric learning: cross-entropy vs. pairwise losses. In European Conference on Computer Vision, 2020.
[4] John Bridle, Anthony Heading, and David MacKay. Unsupervised classifiers, mutual information and phantom targets. In Advances in Neural Information Processing Systems, 1991.
[5] Pin-Yu Chen, Huan Zhang, Yash Sharma, Jinfeng Yi, and Cho-Jui Hsieh. Zoo: Zeroth order optimization based black-box attacks to deep neural networks without training substitute models. In ACM Workshop on Artificial Intelligence and Security, 2017.
[6] John C Duchi, Michael I Jordan, Martin J Wainwright, and Andre Wibisono. Optimal rates for zero-order convex optimization: The power of two function evaluations. IEEE Transactions on Information Theory, 61(5):2788-2806, 2015.
[7] Cian Eastwood, Ian Mason, Christopher KI Williams, and Bernhard Scholkopf. Source-free adaptation to measurement shift via bottom-up feature restoration. In International Conference on Learning Representations, 2022.
[8] Zhen Fang, Yixuan Li, Jie Lu, Jiahua Dong, Bo Han, and Feng Liu. Is out-of-distribution detection learnable? In Advances in Neural Information Processing Systems, 2022.
[9] Yaroslav Ganin and Victor Lempitsky. Unsupervised domain adaptation by backpropagation. In International Conference on Machine Learning, 2015.
[10] Jin Gao, Jialing Zhang, Xihui Liu, Trevor Darrell, Evan Shelhamer, and Dequan Wang. Back to the source: Diffusion-driven test-time adaptation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023.
[11] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2016.
[12] Yufan He, Aaron Carass, Lianrui Zuo, Blake E Dewey, and Jerry L Prince. Autoencoder based self-supervised test-time adaptation for medical image analysis. Medical image analysis, 72:102136, 2021.
[13] Dan Hendrycks, Steven Basart, Norman Mu, Saurav Kadavath, Frank Wang, Evan Dorundo, Rahul Desai, Tyler Zhu, Samyak Parajuli, Mike Guo, et al. The many faces of robustness: A critical analysis of out-of-distribution generalization. In International Conference on Computer Vision, 2021.
[14] Dan Hendrycks and Thomas Dietterich. Benchmarking neural network robustness to common corruptions and perturbations. In International Conference on Learning Representations, 2019.
[15] Jiaxing Huang, Dayan Guan, Aoran Xiao, and Shijian Lu. Model adaptation: Historical contrastive learning for unsupervised domain adaptation without source data. In Advances in Neural Information Processing Systems, 2021.
[16] Yusuke Iwasawa and Yutaka Matsuo. Test-time classifier adjustment module for model-agnostic domain generalization. In Advances in Neural Information Processing Systems, 2021.
[17] Minguk Jang and Sae-Young Chung. Test-time adaptation via self-training with nearest neighbor information. In International Conference on Learning Representations, 2023.