[1] Ahmed A Abdelrahman, Thorsten Hempel, Aly Khalifa, and Ayoub Al-Hamadi. L2cs-net: Fine-grained gaze estimation in unconstrained environments. arXiv preprint arXiv:2203.03339, 2022.
[2] Wenzhi Cao, Vahid Mirjalili, and Sebastian Raschka. Rank consistent ordinal regression for neural networks with application to age estimation. Pattern Recognition Letters, 140:325-331, 2020.
[3] Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin. Emerging properties in self-supervised vision transformers. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 9650-9660, 2021.
[4] Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. A simple framework for contrastive learning of visual representations. In International conference on machine learning, pages 1597-1607. PMLR, 2020.
[5] Xinlei Chen, Haoqi Fan, Ross Girshick, and Kaiming He. Improved baselines with momentum contrastive learning. arXiv preprint arXiv:2003.04297, 2020.
[6] Weiwei Cheng, Eyke Hüllermeier, and Krzysztof J Dembczynski. Label ranking methods based on the plackett-luce model. In Proceedings of the 27th International Conference on Machine Learning (ICML-10), pages 215–222, 2010.
[7] Wei-Ta Chu, Kai-Chia Ho, and Ali Borji. Visual weather temperature prediction. In 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), pages 234-241. IEEE, 2018.
[8] Ching-Yao Chuang, Joshua Robinson, Yen-Chen Lin, Antonio Torralba, and Stefanie Jegelka. Debiased contrastive learning. Advances in neural information processing systems, 33:8765-8775, 2020.
[9] Benoit Dufumier, Pietro Gori, Julie Victor, Antoine Grigis, and Edouard Duchesnay. Conditional alignment and uniformity for contrastive learning with continuous proxy labels. arXiv preprint arXiv:2111.05643, 2021.
[10] Benoit Dufumier, Pietro Gori, Julie Victor, Antoine Grigis, Michele Wessa, Paolo Brambilla, Pauline Favre, Mircea Polosan, Colm Mcdonald, Camille Marie Piguet, et al. Contrastive learning with continuous proxy meta-data for 3d mri classification. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 58-68. Springer, 2021.
[11] Denis A Engemann, Apolline Mellot, Richard Höchenberger, Hubert Banville, David Sabbagh, Lukas Gemein, Tonio Ball, and Alexandre Gramfort. A reusable benchmark of brain-age prediction from m/eeg resting-state signals. NeuroImage, 262:119521, 2022.
[12] Huan Fu, Mingming Gong, Chaohui Wang, Kayhan Batmanghelich, and Dacheng Tao. Deep ordinal regression network for monocular depth estimation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2002-2011, 2018.
[13] Bin-Bin Gao, Chao Xing, Chen-Wei Xie, Jianxin Wu, and Xin Geng. Deep label distribution learning with label ambiguity. IEEE Transactions on Image Processing, 26(6):2825-2838, 2017.
[14] Bin-Bin Gao, Hong-Yu Zhou, Jianxin Wu, and Xin Geng. Age estimation using expectation of label distribution learning. In *IJCAI*, pages 712-718, 2018.
[15] Amir Globerson, Gal Chechik, Fernando Pereira, and Naftali Tishby. Euclidean embedding of co-occurrence data. In NeurIPS, 2004.
[16] Jacob Goldberger, Geoffrey E Hinton, Sam Roweis, and Russ R Salakhutdinov. Neighbourhood components analysis. In NeurIPS, 2004.