Here is some interesting embedding paper which visualized by t-sne, if anyone know other paper, just write down in here.
[1]:Oh Song, Hyun, et al. "Deep metric learning via lifted structured feature embedding." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016.
[2]:Oh Song, Hyun, et al. "Deep metric learning via facility location." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017.
[3]:Wang, Jian, et al. "Deep metric learning with angular loss." 2017 IEEE International Conference on Computer Vision (ICCV). IEEE, 2017.
[4]:Huang, Chen, Chen Change Loy, and Xiaoou Tang. "Local similarity-aware deep feature embedding." Advances in Neural Information Processing Systems. 2016.
[5]:Rippel, Oren, et al. "Metric learning with adaptive density discrimination." arXiv preprint arXiv:1511.05939 (2015).
[6]:Yang, Jufeng, et al. "Retrieving and classifying affective images via deep metric learning." Thirty-Second AAAI Conference on Artificial Intelligence. 2018.
[7]:Wang, Xi, et al. "Matching user photos to online products with robust deep features." Proceedings of the 2016 ACM on International Conference on Multimedia Retrieval. ACM, 2016.