Lars Schmarje, Vasco Grossmann, Claudius Zelenka, Sabine Dippel, Rainer Kiko, Mariusz Oszust, Matti Pastell, Jenny Stracke, Anna Valros, Nina Volkmann, et al. Is one annotation enough? A data-centric image classification benchmark for noisy and ambiguous label estimation. arXiv preprint arXiv:2207.06214, 2022.
Timm Schoening, Autun Purser, Daniel Langenkämper, Inken Suck, James Taylor, Daphne Cuvelier, Lidia Lins, Erik Simon-Lledó, Yann Marcon, Daniel OB Jones, et al. Megafauna community assessment of polymetallic-nodule fields with cameras: platform and methodology comparison. Biogeosciences, 17(12):3115-3133, 2020.
Tyler R Scott, Andrew C Gallagher, and Michael C Mozer. von Mises-Fisher loss: An exploration of embedding geometries for supervised learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021.
Yichun Shi and Anil K Jain. Probabilistic face embeddings. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2019.
Hyun Oh Song, Yu Xiang, Stefanie Jegelka, and Silvio Savarese. Deep metric learning via lifted structured feature embedding. In Computer Vision and Pattern Recognition (CVPR), 2016.
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. Dropout: A simple way to prevent neural networks from overfitting. Journal of Machine Learning Research (JMLR), 15(1):1929-1958, 2014.
Dustin Tran, Jeremiah Liu, Michael W Dusenberry, Du Phan, Mark Collier, Jie Ren, Kehang Han, Zi Wang, Zelda Mariet, Huiyi Hu, et al. Plex: Towards reliability using pretrained large model extensions. arXiv preprint arXiv:2207.07411, 2022.
Gary Ulrich. Computer generation of distributions on the m-sphere. Journal of the Royal Statistical Society: Series C (Applied Statistics), 33(2):158-163, 1984.
Uddeshya Upadhyay, Jae Myung Kim, Cordelia Schmidt, Bernhard Scholkopf, and Zeynep Akata. Posterior annealing: Fast calibrated uncertainty for regression. arXiv preprint arXiv:2302.11012, 2023.
Nina Volkmann, Johannes Brünger, Jenny Stracke, Claudius Zelenka, Reinhard Koch, Nicole Kemper, and Birgit Spindler. Learn to train: Improving training data for a neural network to detect pecking injuries in turkeys. Animals, 11(9):2655, 2021.
Nina Volkmann, Claudius Zelenka, Archana Malavalli Devaraju, Johannes Brünger, Jenny Stracke, Birgit Spindler, Nicole Kemper, and Reinhard Koch. Keypoint detection for injury identification during turkey husbandry using neural networks. Sensors, 22(14):5188, 2022.
C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie. The Caltech-UCSD birds-200-2011 dataset. Technical Report CNS-TR-2011-001, California Institute of Technology, 2011.
Yeming Wen, Ghassen Jerfel, Rafael Muller, Michael W Dusenberry, Jasper Snoek, Balaji Lakshminarayanan, and Dustin Tran. Combining ensembles and data augmentation can harm your calibration. In International Conference on Learning Representations (ICLR), 2021.
Ross Wightman. Pytorch image models, 2019.
Yongqin Xian, Bernt Schiele, and Zeynep Akata. Zero-shot learning - the good, the bad and the ugly. In Computer Vision and Pattern Recognition (CVPR), 2017.
Donggeun Yoo and In So Kweon. Learning loss for active learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019.
Yang You, Jing Li, Sashank Reddi, Jonathan Hseu, Sanjiv Kumar, Srinadh Bhojanapalli, Xiaodan Song, James Demmel, Kurt Keutzer, and Cho-Jui Hsieh. Large batch optimization for deep learning: Training bert in 76 minutes. In International Conference on Learning Representations (ICLR), 2020.
Ke Zou, Zhihao Chen, Xuedong Yuan, Xiaojing Shen, Meng Wang, and Huazhu Fu. A review of uncertainty estimation and its application in medical imaging. arXiv preprint arXiv:2302.08119, 2023.