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One of the challenges in contrastive learning is the selection of appropriate \textit{hard negative} examples, in the absence of label information.
Learning a similarity metric discriminatively, with application to face verification
Sumit Chopra, Raia Hadsell, and Yann LeCun · 2005
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Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2018
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Fan-Yun Sun, Jordan Hoffman, Vikas Verma, and Jian Tang · 2019
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Sangdoo Yun, Dongyoon Han, Seong Joon Oh, Sanghyuk Chun, Junsuk Choe, and Youngjoon Yoo · 2019
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S4l: Self-supervised semi-supervised learning
Xiaohua Zhai, Avital Oliver, Alexander Kolesnikov, and Lucas Beyer · 2019
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Uncertainty-based decision making using deep reinforcement learning
Xujiang Zhao, Shu Hu, Jin-Hee Cho, and Feng Chen · 2019
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Local aggregation for unsupervised learning of visual embeddings
Chengxu Zhuang, Alex Lin Zhai, and Daniel Yamins · 2019
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Deep batch active learning by diverse, uncertain gradient lower bounds
Jordan T Ash, Chicheng Zhang, Akshay Krishnamurthy, John Langford, and Alekh Agarwal · 2020
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Are all negatives created equal in contrastive instance discrimination?
Tiffany Tianhui Cai, Jonathan Frankle, David J Schwab, and Ari S Morcos · 2020
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Unsupervised learning of visual features by contrasting cluster assignments
Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin · 2020
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Debiased contrastive learning
Ching-Yao Chuang, Joshua Robinson, Lin Yen-Chen, Antonio Torralba, and Stefanie Jegelka · 2020
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An empirical study of training self-supervised vision transformers
Xinlei Chen, Saining Xie, and Kaiming He · 2021
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Robust contrastive learning using negative samples with diminished semantics
Songwei Ge, Shlok Mishra, Chun-Liang Li, Haohan Wang, and David Jacobs · 2021
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Self-supervised pretraining of visual features in the wild
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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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Ujjal Kr Dutta, Mehrtash Harandi, and C Chandra Sekhar · 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 Avila Pires, Zhaohan Daniel Guo, Mohammad Gheshlaghi Azar, et al · 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
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Simple and principled uncertainty estimation with deterministic deep learning via distance awareness
Jeremiah Liu, Zi Lin, Shreyas Padhy, Dustin Tran, Tania Bedrax Weiss, and Balaji Lakshminarayanan · 2020
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Gcc: Graph contrastive coding for graph neural network pre-training
Jiezhong Qiu, Qibin Chen, Yuxiao Dong, Jing Zhang, Hongxia Yang, Ming Ding, Kuansan Wang, and Jie Tang · 2020
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Ching-Yun Ko, Jeet Mohapatra, Sijia Liu, Pin-Yu Chen, Luca Daniel, and Lily Weng · 2021
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i-Mix: A domain-agnostic strategy for contrastive representation learning
Kibok Lee, Yian Zhu, Kihyuk Sohn, Chun-Liang Li, Jinwoo Shin, and Honglak Lee · 2021
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Active contrastive learning of audio-visual video representations
Shuang Ma, Zhaoyang Zeng, Daniel McDuff, and Yale Song · 2021
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Tian Pan, Yibing Song, Tianyu Yang, Wenhao Jiang, and Wei Liu · 2021
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Spatiotemporal contrastive video representation learning
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Contrastive learning with hard negative samples
Joshua Robinson, Ching-Yao Chuang, Suvrit Sra, and Stefanie Jegelka · 2021
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Anshul Shah, Suvrit Sra, Rama Chellappa, and Anoop Cherian · 2021
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Zhenda Xie, Yutong Lin, Zheng Zhang, Yue Cao, Stephen Lin, and Han Hu · 2021
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Approximate nearest neighbor negative contrastive learning for dense text retrieval
Lee Xiong, Chenyan Xiong, Ye Li, Kwok-Fung Tang, Jialin Liu, Paul Bennett, Junaid Ahmed, and Arnold Overwijk · 2021
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Improving contrastive learning by visualizing feature transformation
Rui Zhu, Bingchen Zhao, Jingen Liu, Zhenglong Sun, and Chang Wen Chen · 2021
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Boosting contrastive self-supervised learning with false negative cancellation
Tri Huynh, Simon Kornblith, Matthew R. Walter, Michael Maire, and Maryam Khademi · 2022
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FFCV: an optimized data pipeline for accelerating ML training
Guillaume Leclerc, Andrew Ilyas, Logan Engstrom, Sung Min Park, Hadi Salman, and Aleksander Madry · 2022
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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 · 2022
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