[11] Yuhao Cheng, Xiaoguang Zhu, Jiuchao Qian, Fei Wen, and Peilin Liu. Cross-modal graph matching network for image-text retrieval. ACM Transactions on Multimedia Computing, Communications, and Applications, 18(4):1-23, 2022.
[12] Fan RK Chung. Spectral graph theory. American Mathematical Society, 92, 1997.
[13] Shuhao Cui, Shuhui Wang, Junbao Zhuo, Liang Li, Qingming Huang, and Qi Tian. Towards discriminability and diversity: Batch nuclear-norm maximization under label insufficient situations. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 3941-3950. IEEE, 2020.
[14] Shuhao Cui, Shuhui Wang, Junbao Zhuo, Chi Su, Qingming Huang, and Qi Tian. Gradually vanishing bridge for adversarial domain adaptation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 12452-12461. IEEE, 2020.
[15] Shai Ben David, Tyler Lu, Teresa Luu, and David Pal. Impossibility theorems for domain adaptation. In Proceedings of the 13th International Conference on Artificial Intelligence and Statistics (AISTATS), pages 129-136. PMLR, 2010.
[16] Michael Defferrard, Xavier Bresson, and Pierre Vandergheynst. Convolutional neural networks on graphs with fast localized spectral filtering. In Advances in Neural Information Processing Systems (NeurIPS), volume 29, pages 3837-3845, 2016.
[17] Laurens Van der Maaten and Geoffrey Hinton. Visualizing data using t-sne. The Journal of Machine Learning Research (JMLR), 9(11), 2008.
[18] Yaroslav Ganin and Victor Lempitsky. Unsupervised domain adaptation by back propagation. In Proceedings of the 32th International Conference on Machine Learning (ICML), pages 1180-1189. PMLR, 2015.
[19] Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor S. Lempitsky. Domain-adversarial training of neural networks. The Journal of Machine Learning Research (JMLR), 17:59:1-59:35, 2016.
[20] Boqing Gong, Yuan Shi, Fei Sha, and Kristen Grauman. Geodesic flow kernel for unsupervised domain adaptation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 2066-2073. IEEE, 2012.
[21] Yves Grandvalet and Yoshua Bengio. Semi-supervised learning by entropy minimization. In Advances in Neural Information Processing Systems (NeurIPS), 2004.
[22] Jiao Gu, Bobo Hua, and Shiping Liu. Spectral distances on graphs. Discrete Applied Mathematics, 190:56-74, 2015.
[23] Xiang Gu, Jian Sun, and Zongben Xu. Spherical space domain adaptation with robust pseudolabel loss. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 9101-9110. IEEE, 2020.
[24] Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q. Weinberger. On calibration of modern neural networks. In Proceedings of the 34th International Conference on Machine Learning (ICML), pages 1321-1330. PMLR, 2017.
[25] 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 (CVPR), pages 770-778. IEEE, 2016.
[26] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Identity mappings in deep residual networks. In the 14th European Conference on Computer Vision (ECCV), volume 9908, pages 630-645. Springer, 2016.
[27] Ahmet Iscen, Giorgos Tolias, Yannis Avrithis, and Ondrej Chum. Label propagation for deep semi-supervised learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 5070-5079. IEEE, 2019.