[28] Junguang Jiang, Baixu Chen, Bo Fu, and Mingsheng Long. Transfer-learning-library. https://github.com/thuml/Transfer-Learning-Library, 2020.
[29] Junguang Jiang, Yang Shu, Jianmin Wang, and Mingsheng Long. Transferability in deep learning: A survey. CoRR, abs/2201.05867, 2022.
[30] Pin Jiang, Among Wu, Yahong Han, Yunfeng Shao, Meiyu Qi, and Bingshuai Li. Bidirectional adversarial training for semi-supervised domain adaptation. In Proceedings of the 29th International Joint Conference on Artificial Intelligence (IJCAI), page 934–940, 2020.
[31] Ying Jin, Ximei Wang, Mingsheng Long, and Jianmin Wang. Minimum class confusion for versatile domain adaptation. In the 16th European Conference on Computer Vision (ECCV), volume 12366, pages 464-480. Springer, 2020.
[32] Quinonero-Candela Joaquin, Masashi Sugiyama, Anton Schwaighofer, and Neil D. Lawrence. Dataset shift in machine learning. Mit Press, 2008.
[33] Tarun Kalluri, Astuti Sharma, and Manmohan Chandraker. Mem sac: Memory augmented sample consistency for large scale domain adaptation. In the 17th European Conference on Computer Vision (ECCV), pages 550-568. Springer, 2022.
[34] Andrej Karpathy and Fei-Fei Li. Deep visual-semantic alignments for generating image descriptions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 3128–3137. IEEE, 2015.
[35] Taekyung Kim and Changick Kim. Attract, perturb, and explore: Learning a feature alignment network for semisupervised domain adaptation. In the 16th European Conference on Computer Vision (ECCV), pages 591-607. Springer, 2020.
[36] Thomas N. Kipf and Max Welling. Semi-supervised classification with graph convolutional networks. In International Conference on Learning Representations (ICLR), 2017.
[37] Jogendra Nath Kundu, Suvaansh Bhambri, Akshay Kulkarni, Hiran Sarkar, Varun Jampani, and R. Venkatesh Babu. Subsidiary prototype alignment for universal domain adaptation. In Advances in Neural Information Processing Systems (NeurIPS), volume 35, 2022.
[38] Jogendra Nath Kundu, Akshay R Kulkarni, Suvaansh Bhambri, Deepesh Mehta, Shreyas Anand Kulkarni, Varun Jampani, and Venkatesh Babu Radhakrishnan. Balancing discriminability and transferability for source-free domain adaptation. In Proceedings of the 39th International Conference on Machine Learning (ICML), volume 162, pages 11710-11728. PMLR, 2022.
[39] Dong-Hyun Lee. Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks. In Workshop on challenges in representation learning, ICML, page 896, 2013.
[40] Seungmin Lee, Dongwan Kim, Namil Kim, and Seong-Gyun Jeong. Drop to adapt: Learning discriminative features for unsupervised domain adaptation. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), pages 91-100. IEEE, 2019.
[41] Jichang Li, Guanbin Li, Yemin Shi, and Yizhou Yu. Cross-domain adaptive clustering for semi-supervised domain adaptation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 2505-2514, 2021.
[42] Jingjing Li, Erpeng Chen, Dingm Zhengming, Lei Zhy, Ke Lu, and Heng Tao Shen. Maximum density divergence for domain adaptation. IEEE transactions on pattern analysis and machine intelligence (TPAMI), 11(43):3918-3930, 2020.
[43] Linjie Li, Zhe Gan, Yu Cheng, and Jingjing Liu. Relation-aware graph attention network for visual question answering. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), pages 10312-10321. IEEE, 2019.
[44] Mengxue Li, Yi-Ming Zhai, You-Wei Luo, Peng-Fei Ge, and Chuan-Xian Ren. Enhanced transport distance for unsupervised domain adaptation. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pages 13936-13944. IEEE, 2020.