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Contrastive learning (CL) is a form of self-supervised learning and has been widely used for various tasks.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Noise-contrastive estimation: A new estimation principle for unnormalized statistical models
Michael Gutmann and Aapo Hyvärinen · 2010
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Search strategies for multiple landmark detection by submodular maximization
David Liu, S. Kevin Zhou, Dominik Bernhardt, and Dorin Comaniciu · 2010
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U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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Medical image recognition, segmentation and parsing: Machine learning and multiple object approaches, 2015
S. Kevin Zhou · 2015
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A benchmark for comparison of dental radiography analysis algorithms
Ching-Wei Wang, Cheng-Ta Huang, Jia-Hong Lee, Chung-Hsing Li, Sheng-Wei Chang, Ming-Jhih Siao, Tat-Ming Lai, Bulat Ibragimov, Tomaž Vrtovec, Olaf Ronneberger, et al · 2016
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Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
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mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, and David Lopez-Paz · 2018
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Integrating spatial configuration into heatmap regression based cnns for landmark localization
Christian Payer, Darko Štern, Horst Bischof, and Martin Urschler · 2019
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Manifold mixup: Better representations by interpolating hidden states
Vikas Verma, Alex Lamb, Christopher Beckham, Amir Najafi, Ioannis Mitliagkas, David Lopez-Paz, and Yoshua Bengio · 2019
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Cutmix: Regularization strategy to train strong classifiers with localizable features
Sangdoo Yun, Dongyoon Han, Seong Joon Oh, Sanghyuk Chun, Junsuk Choe, and Youngjoon Yoo · 2019
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Contrastive learning of global and local features for medical image segmentation with limited annotations
Krishna Chaitanya, Ertunc Erdil, Neerav Karani, and Ender Konukoglu · 2020
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey E. Hinton · 2020
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Big self-supervised models are strong semi-supervised learners
Ting Chen, Simon Kornblith, Kevin Swersky, Mohammad Norouzi, and Geoffrey E Hinton · 2020
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Improved baselines with momentum contrastive learning
Xinlei Chen, Haoqi Fan, Ross Girshick, and Kaiming He · 2020
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Debiased contrastive learning
Ching-Yao Chuang, Joshua Robinson, Yen-Chen Lin, Antonio Torralba, and Stefanie Jegelka · 2020
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Bootstrap your own latent - A new approach to self-supervised learning
Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre H. Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Ávila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, Bilal Piot, Koray Kavukcuoglu, Rémi Munos, and Michal Valko · 2020
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Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2020
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Hard negative mixing for contrastive learning
Yannis Kalantidis, Mert Bulent Sariyildiz, Noe Pion, Philippe Weinzaepfel, and Diane Larlus · 2020
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Mixco: Mix-up contrastive learning for visual representation
Sungnyun Kim, Gihun Lee, Sangmin Bae, and Se-Young Yun · 2020
Cited alongside, same era.
P4contrast: Contrastive learning with pairs of point-pixel pairs for RGB-D scene understanding
Yunze Liu, Li Yi, Shanghang Zhang, Qingnan Fan, Thomas A. Funkhouser, and Hao Dong · 2020
Cited alongside, same era.
Contrastive learning with hard negative samples
Joshua Robinson, Ching-Yao Chuang, Suvrit Sra, and Stefanie Jegelka · 2020
Cited alongside, same era.
What makes for good views for contrastive learning?
Yonglong Tian, Chen Sun, Ben Poole, Dilip Krishnan, Cordelia Schmid, and Phillip Isola · 2020
Cited alongside, same era.
PGL: prior-guided local self-supervised learning for 3d medical image segmentation
Barlow twins: Self-supervised learning via redundancy reduction
Jure Zbontar, Li Jing, Ishan Misra, Yann LeCun, and Stéphane Deny · 2021
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Contrastive learning for label efficient semantic segmentation
Xiangyun Zhao, Raviteja Vemulapalli, Philip Andrew Mansfield, Boqing Gong, Bradley Green, Lior Shapira, and Ying Wu · 2021
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Pixel contrastive-consistent semi-supervised semantic segmentation
Yuanyi Zhong, Bodi Yuan, Hong Wu, Zhiqiang Yuan, Jian Peng, and Yu-Xiong Wang · 2021
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C3-semiseg: Contrastive semi-supervised segmentation via cross-set learning and dynamic class-balancing
Yanning Zhou, Hang Xu, Wei Zhang, Bin Gao, and Pheng-Ann Heng · 2021
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You only learn once: Universal anatomical landmark detection
Heqin Zhu, Qingsong Yao, Li Xiao, and S Kevin Zhou · 2021
Later among the works it cites.
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Yutong Xie, Jianpeng Zhang, Zehui Liao, Yong Xia, and Chunhua Shen · 2020
Cited alongside, same era.
Graph contrastive learning with augmentations
Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, and Yang Shen · 2020
Cited alongside, same era.
Semi-supervised semantic segmentation with pixel-level contrastive learning from a class-wise memory bank
Inigo Alonso, Alberto Sabater, David Ferstl, Luis Montesano, and Ana C Murillo · 2021
Cited alongside, same era.
Krishna Chaitanya, Ertunc Erdil, Neerav Karani, and Ender Konukoglu · 2021
Cited alongside, same era.
Unsupervised semantic segmentation by contrasting object mask proposals
Wouter Van Gansbeke, Simon Vandenhende, Stamatios Georgoulis, and Luc Van Gool · 2021
Cited alongside, same era.
Region-aware contrastive learning for semantic segmentation
Hanzhe Hu, Jinshi Cui, and Liwei Wang · 2021
Cited alongside, same era.
Semi-supervised contrastive learning for label-efficient medical image segmentation
Xinrong Hu, Dewen Zeng, Xiaowei Xu, and Yiyu Shi · 2021
Cited alongside, same era.
Universal weakly supervised segmentation by pixel-to-segment contrastive learning
Tsung-Wei Ke, Jyh-Jing Hwang, and Stella X. Yu · 2021
Cited alongside, same era.
Rui Zhu, Bingchen Zhao, Jingen Liu, Zhenglong Sun, and Chang Wen Chen · 2021
Later among the works it cites.
When does contrastive visual representation learning work?
Elijah Cole, Xuan Yang, Kimberly Wilber, Oisin Mac Aodha, and Serge Belongie · 2022
Closest in time.
Weakly supervised semantic segmentation by pixel-to-prototype contrast
Ye Du, Zehua Fu, Qingjie Liu, and Yunhong Wang · 2022
Closest in time.
Bootstrapping semantic segmentation with regional contrast
Shikun Liu, Shuaifeng Zhi, Edward Johns, and Andrew J. Davison · 2022
Closest in time.
Contour-hugging heatmaps for landmark detection
James McCouat and Irina Voiculescu · 2022
Closest in time.
Which images to label for few-shot medical landmark detection?
Quan Quan, Qingsong Yao, Jun Li, and S Kevin Zhou · 2022
Closest in time.
Un-mix: Rethinking image mixtures for unsupervised visual representation learning
Zhiqiang Shen, Zechun Liu, Zhuang Liu, Marios Savvides, Trevor Darrell, and Eric Xing · 2022
Closest in time.
Uncertainty-guided pixel contrastive learning for semi-supervised medical image segmentation
Tao Wang, Jianglin Lu, Zhihui Lai, Jiajun Wen, and Heng Kong · 2022
Closest in time.
Contrastive learning with stronger augmentations
Xiao Wang and Guo-Jun Qi · 2022
Closest in time.
SAM: self-supervised learning of pixel-wise anatomical embeddings in radiological images
Ke Yan, Jinzheng Cai, Dakai Jin, Shun Miao, Dazhou Guo, Adam P. Harrison, Youbao Tang, Jing Xiao, Jingjing Lu, and Le Lu · 2022
Closest in time.
Mine your own anatomy: Revisiting medical image segmentation with extremely limited labels
Chenyu You, Weicheng Dai, Fenglin Liu, Haoran Su, Xiaoran Zhang, Lawrence Staib, and James S Duncan · 2022
Closest in time.
Chenyu You, Weicheng Dai, Lawrence Staib, and James S Duncan · 2022
Closest in time.
Momentum contrastive voxel-wise representation learning for semi-supervised volumetric medical image segmentation
Chenyu You, Ruihan Zhao, Lawrence H. Staib, and James S. Duncan · 2022
Closest in time.