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On Information and Sufficiency
S Kullback and R A Leibler · 1951
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A Simple Framework for Contrastive Learning of Visual Representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2002
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Improved Baselines with Momentum Contrastive Learning
Xinlei Chen, Haoqi Fan, Ross Girshick, and Kaiming He · 2003
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Do experts make mistakes? A comparison of human and machine identification of dinoflagellates
Phil Culverhouse, Robert Williams, Beatriz Reguera, Vincent Herry, and Sonsoles González-Gil · 2003
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Mammography: Interobserver variability in breast density assessment
E.A. A Ooms, H.M. M Zonderland, M.J.C. J C Eijkemans, M. Kriege, B. Mahdavian Delavary, C.W. W Burger, and A.C. C Ansink · 2007
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Visualizing data using t-SNE
Laurens der Maaten and Geoffrey Hinton · 2008
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, and Others · 2009
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The Fourth Paradigm – Data-Intensive Scientific Discovery
Tony Hey · 2012
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton · 2012
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Interrater reliability: the kappa statistic
Mary L McHugh · 2012
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Machine learning: a probabilistic perspective
Kevin P Murphy · 2012
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Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures
James Bergstra, Daniel Yamins, and David Cox · 2013
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Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks
Dong-Hyun Lee · 2013
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Ambiguity-aware Ensemble Training for Semi-supervised Dependency Parsing
Zhenghua Li, Min Zhang, and Wenliang Chen · 2014
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Distilling the Knowledge in a Neural Network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
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TensorFlow: A System for Large-Scale Machine Learning
Martin Mart \ \backslash ’ \ \backslash in Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, Manjunath Kudler, Josh Levensberg, Rajat Monga, Sherry Moore, Derek Murray, Benoit Steiner, Paul Tucker, Vijay Vasudevan, Pete Warden, Martin Wicke, Yuan Yu, Xiaoqiang Zheng, and Others · 2016
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On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger · 2017
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What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?
Alex Kendall and Yarin Gal · 2017
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Temporal ensembling for semi-supervised learning
Samuli Laine and Timo Aila · 2017
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WebVision Database: Visual Learning and Understanding from Web Data
Wen Li, Limin Wang, Wei Li, Eirikur Agustsson, and Luc Van Gool · 2017
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Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
Antti Tarvainen and Harri Valpola · 2017
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A new wave of marine evidence-based management: Emerging challenges and solutions to transform monitoring, evaluating, and reporting
P. F.E. E E Addison, D. J. Collins, R. Trebilco, S. Howe, N. Bax, P. Hedge, G. Jones, P. Miloslavich, C. Roelfsema, M. Sams, R. D. Stuart-Smith, P. Scanes, P. Von Baumgarten, and A. McQuatters-Gollop · 2018
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Label refinery: Improving imagenet classification through label progression
Hessam Bagherinezhad, Maxwell Horton, Mohammad Rastegari, and Ali Farhadi · 2018
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Clinically applicable deep learning for diagnosis and referral in retinal disease
Jeffrey De Fauw, Joseph R Ledsam, Bernardino Romera-Paredes, Stanislav Nikolov, Nenad Tomasev, Sam Blackwell, Harry Askham, Xavier Glorot, Brendan O’Donoghue, Daniel Visentin, Others, Brendan O’Donoghue, Daniel Visentin, and Others · 2018
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On the effect of inter-observer variability for a reliable estimation of uncertainty of medical image segmentation
Alain Jungo, Raphael Meier, Ekin Ermis, Marcela Blatti-Moreno, Evelyn Herrmann, Roland Wiest, and Mauricio Reyes · 2018
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Cleannet: Transfer learning for scalable image classifier training with label noise
Kuang-Huei Lee, Xiaodong He, Lei Zhang, and Linjun Yang · 2018
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Instance-based generalization for human judgments about uncertainty
Philipp Schustek and Rubén Moreno-Bote · 2018
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‘Tailception’: using neural networks for assessing tail lesions on pictures of pig carcasses
J Brünger, S Dippel, R Koch, and C Veit · 2019
Cited alongside, same era.
Deep self-learning from noisy labels
Jiangfan Han, Ping Luo, and Xiaogang Wang · 2019
Cited alongside, same era.
Deep Learning-Based Gleason Grading of Prostate Cancer From Histopathology Images—Role of Multiscale Decision Aggregation and Data Augmentation
D Karimi, G Nir, L Fazli, P C Black, L Goldenberg, and S E Salcudean · 2019
Cited alongside, same era.
Revisiting self-supervised visual representation learning
Alexander Kolesnikov, Xiaohua Zhai, and Lucas Beyer · 2019
Cited alongside, same era.
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
Cited alongside, same era.
We Need to Consider Disagreement in Evaluation
Valerio Basile, Michael Fell, Tommaso Fornaciari, Dirk Hovy, Silviu Paun, Barbara Plank, Massimo Poesio, and Alexandra Uma · 2021
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Correlated Input-Dependent Label Noise in Large-Scale Image Classification
Mark Collier, Basil Mustafa, Efi Kokiopoulou, Rodolphe Jenatton, and Jesse Berent · 2021
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Dealing with Disagreements: Looking Beyond the Majority Vote in Subjective Annotations
Aida Mostafazadeh Davani, Mark Díaz, and Vinodkumar Prabhakaran · 2021
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What Are Bayesian Neural Network Posteriors Really Like?
Pavel Izmailov, Sharad Vikram, Matthew D. Hoffman, and Andrew Gordon Gordon Wilson · 2021
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On Data-centric Myths
Adam Marcu, Antonia; Prugel-Bennett · 2021
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A Data-Centric Approach for Training Deep Neural Networks with Less Data
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Human uncertainty makes classification more robust
Joshua Peterson, Ruairidh Battleday, Thomas Griffiths, and Olga Russakovsky · 2019
Cited alongside, same era.
2D and 3D Segmentation of uncertain local collagen fiber orientations in SHG microscopy
Lars Schmarje, Claudius Zelenka, Ulf Geisen, Claus-C. Glüer, and Reinhard Koch · 2019
Cited alongside, same era.
Searching to exploit memorization effect in learning from corrupted labels
Quanming Yao, Hansi Yang, Bo Han, Gang Niu, and James Kwok · 2019
Cited alongside, same era.
How does disagreement help generalization against label corruption?
Xingrui Yu, Bo Han, Jiangchao Yao, Gang Niu, Ivor Tsang, and Masashi Sugiyama · 2019
Cited alongside, same era.
Lucas Beyer, Olivier J. Hénaff, Alexander Kolesnikov, Xiaohua Zhai, and Aäron van den Oord · 2020
Cited alongside, same era.
Unsupervised Learning of Visual Features by Contrasting Cluster Assignments
Mathilde Caron, Priya Goyal, Ishan Misra, Piotr Bojanowski, Julien Mairal, and Armand Joulin · 2020
Cited alongside, same era.
Big Self-Supervised Models are Strong Semi-Supervised Learners
Ting Chen, Simon Kornblith, Kevin Swersky, Mohammad Norouzi, and Geoffrey Hinton · 2020
Cited alongside, same era.
Mohammad Motamedi, Nikolay Sakharnykh, and Tim Kaldewey · 2021
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Uncertainty Baselines: Benchmarks for uncertainty & robustness in deep learning
Zachary Nado, Neil Band, Mark Collier, Josip Djolonga, Michael W Dusenberry, Sebastian Farquhar, Angelos Filos, Marton Havasi, Rodolphe Jenatton, Ghassen Jerfel, and Others · 2021
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Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks
Curtis G. Northcutt, Anish Athalye, and Jonas Mueller · 2021
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Learning Transferable Visual Models From Natural Language Supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever · 2021
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Towards Reducing Aleatoric Uncertainty for Medical Imaging Tasks
Abhishek Singh Sambyal, Narayanan C. Krishnan, and Deepti R. Bathula · 2021
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A Data-Centric Image Classification Benchmark
Lars Schmarje, Yuan-Hong Liao, and Reinhard Koch · 2021
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A Survey on Semi-, Self-and Unsupervised Learning for Image Classification
Lars Schmarje, Monty Santarossa, Simon-Martin Schroder, Reinhard Koch, Simon-Martin Schröder, Reinhard Koch, Simon-Martin Schroder, and Reinhard Koch · 2021
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Fusion of Deep Convolutional Neural Networks for No-Reference Magnetic Resonance Image Quality Assessment
Igor Stȩpień, Rafał Obuchowicz, Adam Piórkowski, and Mariusz Oszust · 2021
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Learning from Disagreement: A Survey
Alexandra N. Uma, Tommaso Fornaciari, Dirk Hovy, Silviu Paun, Barbara Plank, and Massimo Poesio · 2021
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Learn to train: Improving training data for a neural network to detect pecking injuries in turkeys
Nina Volkmann, Johannes Brünger, Jenny Stracke, Claudius Zelenka, Reinhard Koch, Nicole Kemper, and Birgit Spindler · 2021
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Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations
Jiaheng Wei, Zhaowei Zhu, Hao Cheng, Tongliang Liu, Gang Niu, and Yang Liu · 2021
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Re-Labeling ImageNet: From Single to Multi-Labels, From Global to Localized Labels
Sangdoo Yun, Seong Joon Oh, Byeongho Heo, Dongyoon Han, Junsuk Choe, and Sanghyuk Chun · 2021
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CoDiM: Learning with Noisy Labels via Contrastive Semi-Supervised Learning
Xin Zhang, Zixuan Liu, Kaiwen Xiao, Tian Shen, Junzhou Huang, Wei Yang, Dimitris Samaras, and Xiao Han · 2021
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Learning from Multiple Annotator Noisy Labels via Sample-wise Label Fusion
Zhengqi Gao, Fan-Keng Sun, Mingran Yang, Sucheng Ren, Zikai Xiong, Marc Engeler, Antonio Burazer, Linda Wildling, Luca Daniel, and Duane S. Boning · 2022
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Beyond Hard Labels: Investigating data label distributions
Vasco Grossmann, Lars Schmarje, and Reinhard Koch · 2022
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Multi-label iterated learning for image classification with label ambiguity
Sai Rajeswar, Pau Rodriguez, Soumye Singhal, David Vazquez, and Aaron Courville · 2022
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MedRegNet: unsupervised multimodal retinal-image registration with GANs and ranking loss
Monty Santarossa, Ayse Kilic, Claus von der Burchard, Lars Schmarje, Claudius Zelenka, Stefan Reinhold, Reinhard Koch, and Johann Roider · 2022
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A data-centric approach for improving ambiguous labels with combined semi-supervised classification and clustering
Lars Schmarje, Monty Santarossa, Simon-Martin Schröder, Claudius Zelenka, Rainer Kiko, Jenny Stracke, Nina Volkmann, and Reinhard Koch · 2022
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Learning From Noisy Labels With Deep Neural Networks: A Survey
Hwanjun Song, Minseok Kim, Dongmin Park, Jae-Gil Lee, Yooju Shin, and Jae-Gil Lee · 2022
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A Deeper Look into Aleatoric and Epistemic Uncertainty Disentanglement
Matias Valdenegro-Toro and Daniel Saromo · 2022
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Keypoint Detection for Injury Identification during Turkey Husbandry Using Neural Networks
Nina Volkmann, Claudius Zelenka, Archana Malavalli Devaraju, Johannes Brünger, Jenny Stracke, Birgit Spindler, Nicole Kemper, and Reinhard Koch · 2022
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