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Contrastive learning, which aims at minimizing the distance between positive pairs while maximizing that of negative ones, has been widely and successfully applied in unsupervised feature learning, where the design of positive and negative (pos/neg) pairs is one of its keys.
The pascal visual object classes (voc) challenge
Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman · 2010
Earlier work this paper cites.
Noise-contrastive estimation: A new estimation principle for unnormalized statistical models
Michael Gutmann and Aapo Hyvärinen · 2010
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Caltech-ucsd birds 200
Peter Welinder, Steve Branson, Takeshi Mita, Catherine Wah, Florian Schroff, Serge Belongie, and Pietro Perona · 2010
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Fine-grained visual classification of aircraft
S. Maji, J. Kannala, E. Rahtu, M. Blaschko, and A. Vedaldi · 2013
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Semantic image segmentation with deep convolutional nets and fully connected crfs
Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L Yuille · 2014
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Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
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Fully convolutional networks for semantic segmentation
Jonathan Long, Evan Shelhamer, and Trevor Darrell · 2015
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Faster r-cnn: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun · 2015
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Faster r-cnn: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun · 2015
Earlier work this paper cites.
Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al · 2015
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Ssd: Single shot multibox detector
Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, and Alexander C Berg · 2016
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You only look once: Unified, real-time object detection
Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi · 2016
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Mask r-cnn
Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick · 2017
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Kilian Q Weinberger, and Laurens van der Maaten · 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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Deep adversarial metric learning
Yueqi Duan, Wenzhao Zheng, Xudong Lin, Jiwen Lu, and Jie Zhou · 2018
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Deep variational metric learning
Xudong Lin, Yueqi Duan, Qiyuan Dong, Jiwen Lu, and Jie Zhou · 2018
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Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
Earlier work this paper cites.
Megdet: A large mini-batch object detector
Chao Peng, Tete Xiao, Zeming Li, Yuning Jiang, Xiangyu Zhang, Kai Jia, Gang Yu, and Jian Sun · 2018
Cited alongside, same era.
How does batch normalization help optimization?
Shibani Santurkar, Dimitris Tsipras, Andrew Ilyas, and Aleksander Madry · 2018
Cited alongside, same era.
The inaturalist species classification and detection dataset
Grant Van Horn, Oisin Mac Aodha, Yang Song, Yin Cui, Chen Sun, Alex Shepard, Hartwig Adam, Pietro Perona, and Serge Belongie · 2018
Cited alongside, same era.
Unsupervised feature learning via non-parametric instance discrimination
Zhirong Wu, Yuanjun Xiong, Stella X Yu, and Dahua Lin · 2018
Cited alongside, same era.
mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz · 2018
Cited alongside, same era.
Learning representations by maximizing mutual information across views
Philip Bachman, R Devon Hjelm, and William Buchwalter · 2019
Supervised contrastive learning
Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, and Dilip Krishnan · 2020
Later among the works it cites.
Mixco: Mix-up contrastive learning for visual representation
Sungnyun Kim, Gihun Lee, Sangmin Bae, and Se-Young Yun · 2020
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Embedding expansion: Augmentation in embedding space for deep metric learning
Byungsoo Ko and Geonmo Gu · 2020
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Un-mix: Rethinking image mixtures for unsupervised visual representation learning
Zhiqiang Shen, Zechun Liu, Zhuang Liu, Marios Savvides, Trevor Darrell, and Eric Xing · 2020
Later among the works it cites.
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Cited alongside, same era.
Learning deep representations by mutual information estimation and maximization
R Devon Hjelm, Alex Fedorov, Samuel Lavoie-Marchildon, Karan Grewal, Phil Bachman, Adam Trischler, and Yoshua Bengio · 2019
Cited alongside, same era.
Contrastive multiview coding
Yonglong Tian, Dilip Krishnan, and Phillip Isola · 2019
Cited alongside, same era.
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
Cited alongside, same era.
Unsupervised embedding learning via invariant and spreading instance feature
Mang Ye, Xu Zhang, Pong C Yuen, and Shih-Fu Chang · 2019
Cited alongside, same era.
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
Cited alongside, same era.
Joint contrastive learning with infinite possibilities
Qi Cai, Yu Wang, Yingwei Pan, Ting Yao, and Tao Mei · 2020
Cited alongside, same era.
Yonglong Tian, Chen Sun, Ben Poole, Dilip Krishnan, Cordelia Schmid, and Phillip Isola · 2020
Later among the works it cites.
Unsupervised representation learning by invariance propagation
Feng Wang, Huaping Liu, Di Guo, and Fuchun Sun · 2020
Later among the works it cites.
Understanding contrastive representation learning through alignment and uniformity on the hypersphere
Tongzhou Wang and Phillip Isola · 2020
Later among the works it cites.
What makes instance discrimination good for transfer learning?
Nanxuan Zhao, Zhirong Wu, Rynson WH Lau, and Stephen Lin · 2020
Later among the works it cites.
Exploring simple siamese representation learning
Xinlei Chen and Kaiming He · 2021
Closest in time.
Adco: Adversarial contrast for efficient learning of unsupervised representations from self-trained negative adversaries
Qianjiang Hu, Xiao Wang, Wei Hu, and Guo-Jun Qi · 2021
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Mean shift for self-supervised learning
Soroush Abbasi Koohpayegani, Ajinkya Tejankar, and Hamed Pirsiavash · 2021
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i-mix: A strategy for regularizing contrastive representation learning
Kibok Lee, Yian Zhu, Kihyuk Sohn, Chun-Liang Li, Jinwoo Shin, and Honglak Lee · 2021
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Unsupervised feature learning by cross-level instance-group discrimination
Xudong Wang, Ziwei Liu, and Stella X Yu · 2021
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Dense contrastive learning for self-supervised visual pre-training
Xinlong Wang, Rufeng Zhang, Chunhua Shen, Tao Kong, and Lei Li · 2021
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Co2: Consistent contrast for unsupervised visual representation learning
Chen Wei, Huiyu Wang, Wei Shen, and Alan Yuille · 2021
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What should not be contrastive in contrastive learning
Tete Xiao, Xiaolong Wang, Alexei A Efros, and Trevor Darrell · 2021
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Detco: Unsupervised contrastive learning for object detection
Enze Xie, Jian Ding, Wenhai Wang, Xiaohang Zhan, Hang Xu, Zhenguo Li, and Ping Luo · 2021
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Propagate yourself: Exploring pixel-level consistency for unsupervised visual representation learning
Zhenda Xie, Yutong Lin, Zheng Zhang, Yue Cao, Stephen Lin, and Han Hu · 2021
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Instance localization for self-supervised detection pretraining
Ceyuan Yang, Zhirong Wu, Bolei Zhou, and Stephen Lin · 2021
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