[1] Hongjoon Ahn, Sungmin Cha, Donggyu Lee, and Taesup Moon. Uncertainty-based continual learning with adaptive regularization. Advances in neural information processing systems, 32, 2019.
[2] Sara Beery, Arushi Agarwal, Elijah Cole, and Vighnesh Birodkar. The iwildcam 2021 competition dataset. arXiv preprint arXiv:2105.03494, 2021.
[3] Ruisi Cai, Zhenyu Zhang, and Zhangyang Wang. Robust weight signatures: Gaining robustness as easy as patching weights? arXiv preprint arXiv:2302.12480, 2023.
[4] Sebastian Caldas, Sai Meher Karthik Duddu, Peter Wu, Tian Li, Jakub Konečny, H Brendan McMahan, Virginia Smith, and Ameet Talwalkar. Leaf: A benchmark for federated settings. arXiv preprint arXiv:1812.01097, 2018.
[5] Zhangjie Cao, Lijia Ma, Mingsheng Long, and Jianmin Wang. Partial adversarial domain adaptation. In Proceedings of the European conference on computer vision (ECCV), pages 135-150, 2018.
[6] Zhangjie Cao, Kaichao You, Mingsheng Long, Jianmin Wang, and Qiang Yang. Learning to transfer examples for partial domain adaptation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 2985-2994, 2019.
[7] Junbum Cha, Sanghyuk Chun, Kyungjae Lee, Han-Cheol Cho, Seunghyun Park, Yunsung Lee, and Sungrae Park. Swad: Domain generalization by seeking flat minima. Advances in Neural Information Processing Systems, 34:22405-22418, 2021.
[8] Arslan Chaudhry, Marcus Rohrbach, Mohamed Elhoseiny, Thalaiyasingam Ajanthan, Puneet K Dokania, Philip HS Torr, and M Ranzato. Continual learning with tiny episodic memories. 2019.
[9] Hong-You Chen and Wei-Lun Chao. Gradual domain adaptation without indexed intermediate domains. Advances in Neural Information Processing Systems, 34:8201-8214, 2021.
[10] Hong-You Chen and Wei-Lun Chao. On bridging generic and personalized federated learning for image classification. In ICLR, 2022.
[11] Hong-You Chen, Yandong Li, Yin Cui, Mingda Zhang, Wei-Lun Chao, and Li Zhang. Train-once-for-all personalization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11818-11827, 2023.
[12] Hong-You Chen, Cheng-Hao Tu, Ziwei Li, Han-Wei Shen, and Wei-Lun Chao. On the importance and applicability of pre-training for federated learning. In ICLR, 2023.
[13] Hong-You Chen, Jike Zhong, Mingda Zhang, Xuhui Jia, Hang Qi, Boqing Gong, Wei-Lun Chao, and Li Zhang. Federated learning of shareable bases for personalization-friendly image classification. arXiv preprint arXiv:2304.07882, 2023.
[14] Lin Chen, Huaian Chen, Zhixiang Wei, Xin Jin, Xiao Tan, Yi Jin, and Enhong Chen. Reusing the task-specific classifier as a discriminator: Discriminator-free adversarial domain adaptation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7181-7190, 2022.
[15] Shiming Chen, Ziming Hong, Guo-Sen Xie, Wenhan Yang, Qinmu Peng, Kai Wang, Jian Zhao, and Xinge You. Msdn: Mutually semantic distillation network for zero-shot learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7612-7621, 2022.
[16] Shiming Chen, Wenjie Wang, Beihao Xia, Qinmu Peng, Xinge You, Feng Zheng, and Ling Shao. Free: Feature refinement for generalized zero-shot learning. In Proceedings of the IEEE/CVF international conference on computer vision, pages 122-131, 2021.