[17] Xinyang Chen, Sinan Wang, Bo Fu, Mingsheng Long, and Jianmin Wang. Catastrophic forgetting meets negative transfer: Batch spectral shrinkage for safe transfer learning. Advances in Neural Information Processing Systems, 32, 2019.
[18] Sachin Chhabra, Hemanth Venkateswara, and Baoxin Li. Generative alignment of posterior probabilities for source-free domain adaptation. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 4125-4134, 2023.
[19] Gregory Cohen, Saeed Afshar, Jonathan Tapson, and Andre Van Schaik. Emmist: Extending mnist to handwritten letters. In 2017 international joint conference on neural networks (IJCNN), pages 2921-2926. IEEE, 2017.
[20] Liam Collins, Hamed Hassani, Aryan Mokhtari, and Sanjay Shakkottai. Fedavg with fine tuning: Local updates lead to representation learning. In Alice H. Oh, Alekh Agarwal, Danielle Belgrave, and Kyunghyun Cho, editors, Advances in Neural Information Processing Systems, 2022.
[21] Liam Collins, Aryan Mokhtari, Sewoong Oh, and Sanjay Shakkottai. MAML and ANIL provably learn representations. In Kamalika Chaudhuri, Stefanie Jegelka, Le Song, Csaba Szepesvari, Gang Niu, and Sivan Sabato, editors, Proceedings of the 39th International Conference on Machine Learning, volume 162 of Proceedings of Machine Learning Research, pages 4238-4310. PMLR, 17-23 Jul 2022.
[22] Prithviraj Dhar, Rajat Vikram Singh, Kuan-Chuan Peng, Ziyan Wu, and Rama Chellappa. Learning without memorizing. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5138-5146, 2019.
[23] Ning Ding, Yixing Xu, Yehui Tang, Chao Xu, Yunhe Wang, and Dacheng Tao. Source-free domain adaptation via distribution estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7212-7222, 2022.
[24] Arthur Douillard, Alexandre Rame, Guillaume Couairon, and Matthieu Cord. Dytox: Transformers for continual learning with dynamic token expansion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9285-9295, 2022.
[25] Utku Evci, Vincent Dumoulin, Hugo Larochelle, and Michael C Mozer. Head2Toe: Utilizing intermediate representations for better transfer learning. In Kamalika Chaudhuri, Stefanie Jegelka, Le Song, Csaba Szepesvari, Gang Niu, and Sivan Sabato, editors, Proceedings of the 39th International Conference on Machine Learning, volume 162 of Proceedings of Machine Learning Research, pages 6009-6033. PMLR, 17-23 Jul 2022.
[26] Michael Feldman, Sorelle A Friedler, John Moeller, Carlos Scheidegger, and Suresh Venkatasubramanian. Certifying and removing disparate impact. In proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining, pages 259-268, 2015.
[27] Yossi Gandelsman, Yu Sun, Xinlei Chen, and Alexei Efros. Test-time training with masked autoencoders. Advances in Neural Information Processing Systems, 35:29374-29385, 2022.
[28] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016.
[29] Kevin Hsieh, Amar Phanishayee, Onur Mutlu, and Phillip Gibbons. The non-iid data quagmire of decentralized machine learning. In International Conference on Machine Learning, pages 4387-4398. PMLR, 2020.
[30] Gabriel Ilharco, Mitchell Wortsman, Samir Yitzhak Gadre, Shuran Song, Hannaneh Hajishirzi, Simon Kornblith, Ali Farhadi, and Ludwig Schmidt. Patching open-vocabulary models by interpolating weights. arXiv preprint arXiv:2208.05592, 2022.
[31] Sergey Ioffe and Christian Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In International conference on machine learning, pages 448-456. pmlr, 2015.
[32] Masato Ishii, Takashi Takenouchi, and Masashi Sugiyama. Partially zero-shot domain adaptation from incomplete target data with missing classes. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 3052-3060, 2020.
[33] Pavel Izmailov, Dmitrii Podoprikhin, Timur Garipov, Dmitry Vetrov, and Andrew Gordon Wilson. Averaging weights leads to wider optima and better generalization. arXiv preprint arXiv:1803.05407, 2018.