[28] Wesley J Maddox, Pavel Izmailov, Timur Garipov, Dmitry P Vetrov, and Andrew Gordon Wilson. A simple baseline for Bayesian uncertainty in deep learning. Advances in Neural Information Processing Systems, 32, 2019.
[29] Marco Miani, Frederik Warburg, Pablo Moreno-Muñoz, Nicke Skafte Detlefsen, and Søren Hauberg. Laplacian autoencoders for learning stochastic representations. In Advances in Neural Information Processing Systems, 2022.
[30] Kevin Musgrave, Serge Belongie, and Ser-Nam Lim. A metric learning reality check. In European Conference on Computer Vision, pages 681-699. Springer, 2020.
[31] Eric Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Gorur, and Balaji Lakshminarayanan. Do deep generative models know what they don't know? arXiv preprint arXiv:1810.09136, 2018.
[32] Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng. Reading digits in natural images with unsupervised feature learning, 2011.
[33] Seong Joon Oh, Kevin Murphy, Jiyan Pan, Joseph Roth, Florian Schroff, and Andrew Gallagher. Modeling uncertainty with hedged instance embedding. arXiv preprint arXiv:1810.00319, 2018.
[34] Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer. Automatic differentiation in PyTorch. In NIPS-W, 2017.
[35] Filip Radenović, Giorgos Tolias, and Ondřej Chum. Fine-tuning CNN image retrieval with no human annotation. IEEE transactions on pattern analysis and machine intelligence, 41(7): 1655-1668, 2018.
[36] Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra. Stochastic backpropagation and approximate inference in deep generative models. In International conference on machine learning, pages 1278-1286. PMLR, 2014.
[37] Florian Schroff, Dmitry Kalenichenko, and James Philbin. Facenet: A unified embedding for face recognition and clustering. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 815-823, 2015.
[38] Yichun Shi and Anil K Jain. Probabilistic face embeddings. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 6902-6911, 2019.
[39] Yale Song and Mohammad Soleymani. Polysemous visual-semantic embedding for cross-modal retrieval. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1979–1988, 2019.
[40] Suvrit Sra. A short note on parameter approximation for von Mises-Fisher distributions: and a fast implementation of i s (x). Computational Statistics, 27(1):177-190, 2012.
[41] Jennifer J Sun, Jiaping Zhao, Liang-Chieh Chen, Florian Schroff, Hartwig Adam, and Ting Liu. View-invariant probabilistic embedding for human pose. In European Conference on Computer Vision, pages 53-70. Springer, 2020.
[42] Ahmed Taha, Yi-Ting Chen, Teruhisa Mitsu, Abhinav Shrivastava, and Larry Davis. Unsupervised data uncertainty learning in visual retrieval systems. arXiv preprint arXiv:1902.02586, 2019.
[43] Ahmed Taha, Yi-Ting Chen, Xitong Yang, Teruhisa Mitsu, and Larry Davis. Exploring uncertainty in conditional multi-modal retrieval systems. arXiv preprint arXiv:1901.07702, 2019.
[44] Luke Vilnis and Andrew McCallum. Word representations via Gaussian embedding. arXiv preprint arXiv:1412.6623, 2014.
[45] Catherine Wah, Steve Branson, Peter Welinder, Pietro Perona, and Serge Belongie. Caltech-UCSD birds 200, 2011.