[45] Shuang Li, Fangrui Lv, Binhui Xie, Chi Harold Liu, Jian Liang, and Chen Qin. Bi-classifier determinacy maximization for unsupervised domain adaptation. In Proceedings of 35th AAAI Conference on Artificial Intelligence, pages 8455-8464. AAAI Press, 2021.
[46] Wuyang Li, Xinyu Liu, and Yixuan Yuan. SIGMA: semantic-complete graph matching for domain adaptive object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 5281-5290. IEEE, 2022.
[47] Jian Liang, Ran He, Zhenan Sun, and Tieniu Tan. Exploring uncertainty in pseudo-label guided unsupervised domain adaptation. Pattern Recognition, 96, 2019.
[48] Jian Liang, Dapeng Hu, and Jiashi Feng. Domain adaptation with auxiliary target domain-oriented classifier. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 16632-16642. IEEE, 2021.
[49] Tsung-Yi Lin, Michael Maire, Serge Belongie, Lubomir Bourdev, Ross Girshick, James Hays, Pietro Perona, Deva Ramanan, C. Lawrence Zitnick, and Piotr Dólar. Microsoft coco: Common objects in context. In the 13th European Conference on Computer Vision (ECCV), pages 740-755. Springer, 2014.
[50] Yanbin Liu, Juho Lee, Minseop Park, Saehoon Kim, Eunho Yang, Sungju Hwang, and Yi Yang. Learning to propagate labels: Transductive propagation network for few-shot learning. In International Conference on Learning Representations (ICLR), 2019.
[51] Mingsheng Long, Yue Cao, Jianmin Wang, and Michael I. Jordan. Learning transferable features with deep adaptation networks. In Proceedings of the 32th International Conference on Machine Learning (ICML), pages 97-105. PMLR, 2015.
[52] Mingsheng Long, Zhangjie Cao, Jianmin Wang, and Michael I. Jordan. Conditional adversarial domain adaptation. In Advances in Neural Information Processing Systems (NeurIPS), volume 31, pages 1647-1657, 2018.
[53] Mingsheng Long, Han Zhu, Jianmin Wang, and Michael I. Jordan. Deep transfer learning with joint adaptation networks. In Proceedings of the 34th International Conference on Machine Learning (ICML), pages 2208-2217. PMLR, 2017.
[54] Lingkun Luo, Liming Chen, and Shiqiang Hu. Attention regularized laplace graph for domain adaptation. In Proceedings of 31th IEEE International Conference on Image Processing (ICIP), pages 7322-7337. IEEE, 2022.
[55] Miller McPherson, Lynn SmithLovin, and James M Cook. Birds of a feather: Homophily in social networks. Annual review of sociology, 27(1):415-444, 2001.
[56] Jaemin Na, Heechul Jung, Hyung Jin Chang, and Wonjun Hwang. Fixbi: Bridging domain spaces for unsupervised domain adaptation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 1094-1103. IEEE, 2021.
[57] Xingchao Peng, Qinxun Bai, Xide Xia, Zijun Huang, Kate Saenko, and Bo Wang. Moment matching for multi-source domain adaptation. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), pages 1406-1415. IEEE, 2019.
[58] Xingchao Peng, Ben Usman, Neela Kaushik, Dequan Wang, Judy Hoffman, and Kate Saenko. Visda: A synthetic-to-real benchmark for visual domain adaptation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPR Workshops), pages 2021-2026. IEEE, 2018.
[59] Hieu Pham, Zihang Dai, Qizhe Xie, and Quoc V. Le. Meta pseudo labels. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 11557-11568. IEEE, 2021.
[60] Zhenyu Qiu, Wenbin Hu, Jia Wu, ZhongZheng Tang, and Xiaohua Jia. Noise-resilient similarity preserving network embedding for social networks. In Proceedings of the 28nd International Joint Conference on Artificial Intelligence (IJCAI), pages 3282-3288, 2019.