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Self-supervised learning approach like contrastive learning is attached great attention in natural language processing.
Learning a similarity metric discriminatively, with application to face verification. In 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05) , Vol. 1. IEEE, 539–546
Sumit Chopra, Raia Hadsell, and Yann LeCun. 2005 · 2005
Earlier work this paper cites.
Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus. 2013 · 2013
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy. 2014 · 2014
Earlier work this paper cites.
Adversarial training methods for semi-supervised text classification
Takeru Miyato, Andrew M Dai, and Ian Goodfellow. 2016 · 2016
Earlier work this paper cites.
Perceptual generative adversarial networks for small object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition . 1222–1230
Jianan Li, Xiaodan Liang, Yunchao Wei, Tingfa Xu, Jiashi Feng, and Shuicheng Yan. 2017 · 2017
Earlier work this paper cites.
Time-contrastive networks: Self-supervised learning from multi-view observation. In 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) . IEEE, 486–487
Pierre Sermanet, Corey Lynch, Jasmine Hsu, and Sergey Levine. 2017 · 2017
Earlier work this paper cites.
Learning from simulated and unsupervised images through adversarial training. In Proceedings of the IEEE conference on computer vision and pattern recognition . 2107–2116
Ashish Shrivastava, Tomas Pfister, Oncel Tuzel, Joshua Susskind, Wenda Wang, and Russell Webb. 2017 · 2017
Earlier work this paper cites.
Adversarial examples for semantic segmentation and object detection. In Proceedings of the IEEE International Conference on Computer Vision . 1369–1378
Cihang Xie, Jianyu Wang, Zhishuai Zhang, Yuyin Zhou, Lingxi Xie, and Alan Yuille. 2017 · 2017
Earlier work this paper cites.
Generating Natural Language Adversarial Examples. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing . 2890–2896
Moustafa Alzantot, Yash Sharma, Ahmed Elgohary, Bo-Jhang Ho, Mani Srivastava, and Kai-Wei Chang. 2018 · 2018
Earlier work this paper cites.
Adversarial attacks and defences: A survey
Anirban Chakraborty, Manaar Alam, Vishal Dey, Anupam Chattopadhyay, and Debdeep Mukhopadhyay. 2018 · 2018
Earlier work this paper cites.
Towards Robust Neural Machine Translation. In ACL (1)
Yong Cheng, Zhaopeng Tu, Fandong Meng, Junjie Zhai, and Yang Liu. 2018 · 2018
Earlier work this paper cites.
HotFlip: White-Box Adversarial Examples for Text Classification. In ACL (2)
Javid Ebrahimi, Anyi Rao, Daniel Lowd, and Dejing Dou. 2018 · 2018
Earlier work this paper cites.
Towards Deep Learning Models Resistant to Adversarial Attacks. In International Conference on Learning Representations
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. 2018 · 2018
Earlier work this paper cites.
Unsupervised feature learning via non-parametric instance-level discrimination
Zhirong Wu, Yuanjun Xiong, Stella Yu, and Dahua Lin. 2018 · 2018
Earlier work this paper cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Earlier work this paper cites.
Roberta: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 2019
Earlier work this paper cites.
Sentence-bert: Sentence embeddings using siamese bert-networks
Nils Reimers and Iryna Gurevych. 2019 · 2019
Cited alongside, same era.
How to fine-tune bert for text classification?. In China National Conference on Chinese Computational Linguistics . Springer, 194–206
Chi Sun, Xipeng Qiu, Yige Xu, and Xuanjing Huang. 2019 · 2019
Cited alongside, same era.
Natural language adversarial attacks and defenses in word level
Xiaosen Wang, Hao Jin, and Kun He. 2019 · 2019
Cited alongside, same era.
Xlnet: Generalized autoregressive pretraining for language understanding
Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Russ R Salakhutdinov, and Quoc V Le. 2019 · 2019
Cited alongside, same era.
Adversarial examples: Attacks and defenses for deep learning
Xiaoyong Yuan, Pan He, Qile Zhu, and Xiaolin Li. 2019 · 2019
Cited alongside, same era.
Adversarial training for large neural language models
Xiaodong Liu, Hao Cheng, Pengcheng He, Weizhu Chen, Yu Wang, Hoifung Poon, and Jianfeng Gao. 2020 · 2020
Later among the works it cites.
Yifei Min, Lin Chen, and Amin Karbasi. 2020 · 2020
Later among the works it cites.
InfoBERT: Improving Robustness of Language Models from An Information Theoretic Perspective. In International Conference on Learning Representations
Boxin Wang, Shuohang Wang, Yu Cheng, Zhe Gan, Ruoxi Jia, Bo Li, and Jingjing Liu. 2020 · 2020
Later among the works it cites.
An Unsupervised Sentence Embedding Method by Mutual Information Maximization. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) . 1601–1610
Yan Zhang, Ruidan He, Zuozhu Liu, Kwan Hui Lim, and Lidong Bing. 2020a · 2020
Later among the works it cites.
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PAWS: Paraphrase Adversaries from Word Scrambling. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) . 1298–1308
Yuan Zhang, Jason Baldridge, and Luheng He. 2019 · 2019
Cited alongside, same era.
Freelb: Enhanced adversarial training for language understanding
Chen Zhu, Yu Cheng, Zhe Gan, Siqi Sun, Tom Goldstein, and Jingjing Liu. 2019 · 2019
Cited alongside, same era.
Improved baselines with momentum contrastive learning
Xinlei Chen, Haoqi Fan, Ross Girshick, and Kaiming He. 2020a · 2020
Cited alongside, same era.
Self-supervised Adversarial Robustness for the Low-label, High-data Regime. In International Conference on Learning Representations
Sven Gowal, Po-Sen Huang, Aaron van den Oord, Timothy Mann, and Pushmeet Kohli. 2020 · 2020
Cited alongside, same era.
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
Cited alongside, same era.
Supervised Contrastive Learning for Pre-trained Language Model Fine-tuning. In International Conference on Learning Representations
Beliz Gunel, Jingfei Du, Alexis Conneau, and Veselin Stoyanov. 2020 · 2020
Cited alongside, same era.
Momentum contrast for unsupervised visual representation learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 9729–9738
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. 2020 · 2020
Cited alongside, same era.
An Unsupervised Sentence Embedding Method by Mutual Information Maximization. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) . 1601–1610
Yan Zhang, Ruidan He, Zuozhu Liu, Kwan Hui Lim, and Lidong Bing. 2020b · 2020
Later among the works it cites.
Incorporating bert into neural machine translation
Jinhua Zhu, Yingce Xia, Lijun Wu, Di He, Tao Qin, Wengang Zhou, Houqiang Li, and Tie-Yan Liu. 2020 · 2020
Later among the works it cites.
Exploring simple siamese representation learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 15750–15758
Xinlei Chen and Kaiming He. 2021 · 2021
Closest in time.
SimCSE: Simple Contrastive Learning of Sentence Embeddings
Tianyu Gao, Xingcheng Yao, and Danqi Chen. 2021 · 2021
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Adco: Adversarial contrast for efficient learning of unsupervised representations from self-trained negative adversaries. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 1074–1083
Qianjiang Hu, Xiao Wang, Wei Hu, and Guo-Jun Qi. 2021 · 2021
Closest in time.
Zhao Meng, Yihan Dong, Mrinmaya Sachan, and Roger Wattenhofer. 2021 · 2021
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Adversarial Training with Contrastive Learning in NLP
Daniela N Rim, DongNyeong Heo, and Heeyoul Choi. 2021 · 2021
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Cline: Contrastive learning with semantic negative examples for natural language understanding
Dong Wang, Ning Ding, Piji Li, and Hai-Tao Zheng. 2021 · 2021
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ConSERT: A Contrastive Framework for Self-Supervised Sentence Representation Transfer
Yuanmeng Yan, Rumei Li, Sirui Wang, Fuzheng Zhang, Wei Wu, and Weiran Xu. 2021 · 2021
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Bootstrapped unsupervised sentence representation learning. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) . 5168–5180
Yan Zhang, Ruidan He, Zuozhu Liu, Lidong Bing, and Haizhou Li. 2021 · 2021
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Domain-adversarial training of neural networks
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor Lempitsky. 2016 · 2030
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