[35] Dandan Guo, Zhuo Li, He Zhao, Mingyuan Zhou, Hongyuan Zha, et al. Learning to re-weight examples with optimal transport for imbalanced classification. Advances in Neural Information Processing Systems, 35:25517-25530, 2022.
[36] Tuan Nguyen, Trung Le, He Zhao, Quan Hung Tran, Truyen Nguyen, and Dinh Phung. Most: multi-source domain adaptation via optimal transport for student-teacher learning. In Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, volume 161 of Proceedings of Machine Learning Research, pages 225–235, 27–30 Jul 2021.
[37] Dandan Guo, Long Tian, He Zhao, Mingyuan Zhou, and Hongyuan Zha. Adaptive distribution calibration for few-shot learning with hierarchical optimal transport. In Advances in Neural Information Processing Systems, pages 6996-7010, 2022.
[38] Zheng Ge, Songtao Liu, Zeming Li, Osamu Yoshie, and Jian Sun. Ota: Optimal transport assignment for object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 303-312, 2021.
[39] Miaoge Li, Dongsheng Wang, Xinyang Liu, Zequn Zeng, Ruiying Lu, Bo Chen, and Mingyuan Zhou. Patchct: Aligning patch set and label set with conditional transport for multi-label image classification. In Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023.
[40] Long Tung Vuong, Trung Le, He Zhao, Chuanxia Zheng, Mehrtash Harandi, Jianfei Cai, and Dinh Q. Phung. Vector quantized wasserstein auto-encoder. In International Conference on Machine Learning, ICML 2023, 23-29 July 2023, Honolulu, Hawaii, USA, volume 202 of Proceedings of Machine Learning Research, pages 35223-35242. PMLR, 2023.
[41] Viet Huynh, Dinh Q. Phung, and He Zhao. Optimal transport for deep generative models: State of the art and research challenges. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI 2021, Virtual Event / Montreal, Canada, 19-27 August 2021, pages 4450-4457, 2021.
[42] Marco Cuturi. Sinkhorn distances: Lightspeed computation of optimal transport. Advances in neural information processing systems, 26, 2013.
[43] Dan dan Guo, Long Tian, Minghe Zhang, Mingyuan Zhou, and Hongyuan Zha. Learning prototype-oriented set representations for meta-learning. In International Conference on Learning Representations, 2022.
[44] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, pages 248–255. IEEE, 2009.
[45] Li Fei-Fei, Rob Fergus, and Pietro Perona. Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories. In 2004 conference on computer vision and pattern recognition workshop, pages 178-178. IEEE, 2004.
[46] Omkar M Parkhi, Andrea Vedaldi, Andrew Zisserman, and CV Jawahar. Cats and dogs. In 2012 IEEE conference on computer vision and pattern recognition, pages 3498-3505. IEEE, 2012.
[47] Jonathan Krause, Michael Stark, Jia Deng, and Li Fei-Fei. 3d object representations for fine-grained categorization. In Proceedings of the IEEE international conference on computer vision workshops, pages 554–561, 2013.
[48] Maria-Elena Nilsback and Andrew Zisserman. Automated flower classification over a large number of classes. In 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing, pages 722-729. IEEE, 2008.
[49] Lukas Bossard, Matthieu Guillaumin, and Luc Van Gool. Food-101-mining discriminative components with random forests. In Computer Vision-ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part VI 13, pages 446-461. Springer, 2014.