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The learn-to-compare paradigm of contrastive representation learning (CRL), which compares positive samples with negative ones for representation learning, has achieved great success in a wide range of domains, including natural language processing, computer vision, information retrieval and graph learning.
Example-based learning for view-based human face detection
K-K Sung and Tomaso Poggio · 1998
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Learning a similarity metric discriminatively, with application to face verification
Sumit Chopra, Raia Hadsell, and Yann LeCun · 2005
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One-class collaborative filtering
Rong Pan, Yunhong Zhou, Bin Cao, Nathan N Liu, Rajan Lukose, Martin Scholz, and Qiang Yang · 2008
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Large scale image annotation: learning to rank with joint word-image embeddings
Jason Weston, Samy Bengio, and Nicolas Usunier · 2010
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Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean · 2013
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Optimizing top-n collaborative filtering via dynamic negative item sampling
Weinan Zhang, Tianqi Chen, Jun Wang, and Yong Yu · 2013
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Rich feature hierarchies for accurate object detection and semantic segmentation
Ross Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik · 2014
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Improving pairwise learning for item recommendation from implicit feedback
Steffen Rendle and Christoph Freudenthaler · 2014
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Facenet: A unified embedding for face recognition and clustering
Florian Schroff, Dmitry Kalenichenko, and James Philbin · 2015
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Line: Large-scale information network embedding
Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, and Qiaozhu Mei · 2015
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Noise-contrastive estimation for answer selection with deep neural networks
Jinfeng Rao, Hua He, and Jimmy Lin · 2016
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Training region-based object detectors with online hard example mining
Abhinav Shrivastava, Abhinav Gupta, and Ross Girshick · 2016
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Kbgan: Adversarial learning for knowledge graph embeddings
Liwei Cai and William Yang Wang · 2017
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On sampling strategies for neural network-based collaborative filtering
Ting Chen, Yizhou Sun, Yue Shi, and Liangjie Hong · 2017
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Irgan: A minimax game for unifying generative and discriminative information retrieval models
Jun Wang, Lantao Yu, Weinan Zhang, Yu Gong, Yinghui Xu, Benyou Wang, Peng Zhang, and Dell Zhang · 2017
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Word2vec applied to recommendation: Hyperparameters matter
Hugo Caselles-Dupré, Florian Lesaint, and Jimena Royo-Letelier · 2018
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Improving negative sampling for word representation using self-embedded features
Long Chen, Fajie Yuan, Joemon M Jose, and Weinan Zhang · 2018
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Deep adversarial metric learning
Yueqi Duan, Wenzhao Zheng, Xudong Lin, 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
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Bootstrapping entity alignment with knowledge graph embedding
Zequn Sun, Wei Hu, Qingheng Zhang, and Yuzhong Qu · 2018
Cited alongside, same era.
Graphgan: Graph representation learning with generative adversarial nets
Hongwei Wang, Jia Wang, Jialin Wang, Miao Zhao, Weinan Zhang, Fuzheng Zhang, Xing Xie, and Minyi Guo · 2018
Cited alongside, same era.
Neural memory streaming recommender networks with adversarial training
Qinyong Wang, Hongzhi Yin, Zhiting Hu, Defu Lian, Hao Wang, and Zi Huang · 2018
Cited alongside, same era.
Graph convolutional neural networks for web-scale recommender systems
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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Contrastive representation learning: A framework and review
Phuc H Le-Khac, Graham Healy, and Alan F Smeaton · 2020
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Contrastive learning with hard negative samples
Joshua Robinson, Ching-Yao Chuang, Suvrit Sra, and Stefanie Jegelka · 2020
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Understanding contrastive representation learning through alignment and uniformity on the hypersphere
Tongzhou Wang and Phillip Isola · 2020
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Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L Hamilton, and Jure Leskovec · 2018
Cited alongside, same era.
Gneg: Graph-based negative sampling for word2vec
Zheng Zhang and Pierre Zweigenbaum · 2018
Cited alongside, same era.
Adversarial learning on heterogeneous information networks
Binbin Hu, Yuan Fang, and Chuan Shi · 2019
Cited alongside, same era.
Pytorch-biggraph: A large scale graph embedding system
Adam Lerer, Ledell Wu, Jiajun Shen, Timothee Lacroix, Luca Wehrstedt, Abhijit Bose, and Alex Peysakhovich · 2019
Cited alongside, same era.
Adversarial sampling and training for semi-supervised information retrieval
Dae Hoon Park and Yi Chang · 2019
Cited alongside, same era.
Transedge: Translating relation-contextualized embeddings for knowledge graphs
Zequn Sun, Jiacheng Huang, Wei Hu, Muhao Chen, Lingbing Guo, and Yuzhong Qu · 2019
Cited alongside, same era.
Neural graph collaborative filtering
Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, and Tat-Seng Chua · 2019
Cited alongside, same era.
Xiang Wang, Yaokun Xu, Xiangnan He, Yixin Cao, Meng Wang, and Tat-Seng Chua · 2020
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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 · 2020
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Adaptive batch scheduling for open-domain question answering
Donghyun Choi, Myeongcheol Shin, Eunggyun Kim, and Dong Ryeol Shin · 2021
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Cuco: Graph representation with curriculum contrastive learning
Guanyi Chu, Xiao Wang, Chuan Shi, and Xunqiang Jiang · 2021
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Simcse: Simple contrastive learning of sentence embeddings
Tianyu Gao, Xingcheng Yao, and Danqi Chen · 2021
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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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Mixgcf: An improved training method for graph neural network-based recommender systems
Tinglin Huang, Yuxiao Dong, Ming Ding, Zhen Yang, Wenzheng Feng, Xinyu Wang, and Jie Tang · 2021
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A survey on contrastive self-supervised learning
Ashish Jaiswal, Ashwin Ramesh Babu, Mohammad Zaki Zadeh, Debapriya Banerjee, and Fillia Makedon · 2021
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Efficient non-sampling knowledge graph embedding
Zelong Li, Jianchao Ji, Zuohui Fu, Yingqiang Ge, Shuyuan Xu, Chong Chen, and Yongfeng Zhang · 2021
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Self-supervised learning: Generative or contrastive
Xiao Liu, Fanjin Zhang, Zhenyu Hou, Li Mian, Zhaoyu Wang, Jing Zhang, and Jie Tang · 2021
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Simplex: A simple and strong baseline for collaborative filtering
Kelong Mao, Jieming Zhu, Jinpeng Wang, Quanyu Dai, Zhenhua Dong, Xi Xiao, and Xiuqiang He · 2021
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Pure: Positive-unlabeled recommendation with generative adversarial network
Yao Zhou, Jianpeng Xu, Jun Wu, Zeinab Taghavi, Evren Korpeoglu, Kannan Achan, and Jingrui He · 2021
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