Note: "*" refers to official code.
- Investigating the Role of Negatives in Contrastive Representation Learning, Jordan T. Ash, 2021
- Contrastive Semi-Supervised Learning for 2D Medical Image Segmentation, Prashant Pandey, MICCAI-2021
- Contrastive Learning with Hard Negative Samples, Joshua Robinson, ICLR2021, [pytorch*]
- Contrastive Representation Learning: A Framework and Review, Phuc H. Le-Khac
- Supervised Contrastive Learning, Prannay Khosla, 2020, [pytorch*]
- A Simple Framework for Contrastive Learning of Visual Representations, Ting Chen, 2020, [pytroch, tensorflow*]
- Improved Baselines with Momentum Contrastive Learning, Xinlei Chen, 2020, [tensorflow]
- Contrastive Representation Distillation, Yonglong Tian, ICLR-2020 [pytorch*]
- COBRA: Contrastive Bi-Modal Representation Algorithm, Vishaal Udandarao, 2020
- What makes for good views for contrastive learning, Yonglong Tian, 2020
- Prototypical Contrastive Learning of Unsupervised Representations, Junnan Li, 2020
- Contrastive Multi-View Representation Learning on Graphs, Kaveh Hassani, 2020
- On Mutual Information in Contrastive Learning for Visual Representations, Mike Wu, 2020
- Semi-Supervised Contrastive Learning with Generalized Contrastive Loss and Its Application to Speaker Recognition, Nakamasa Inoue, 2020
- Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere,Tongzhou Wang, ICML2020, [pytorch*]
- Momentum Contrast for Unsupervised Visual Representation Learning, Kaiming He, 2019, [pytorch]
- Data-Efficient Image Recognition with Contrastive Predictive Coding, Olivier J. Hénaff, 2019
- Contrastive Multiview Coding, Yonglong Tian, 2019, [pytorch*]
- Learning deep representations by mutual information estimation and maximization, R Devon Hjelm, ICLR-2019, [pytorch]
- Contrastive Adaptation Network for Unsupervised Domain Adaptation, Guoliang Kang, CVPR-2019
- Representation learning with contrastive predictive coding, Aaron van den Oord, 2018, [pytorch]
- Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination, Zhirong Wu, CVPR-2018, [pytorch*]
- Adversarial Contrastive Estimation, Avishek Joey Bose, ACL-2018,
- Time-Contrastive Networks: Self-Supervised Learning from Video, Pierre Sermanet, CVPR-2017
- Contrastive Learning for Image Captioning, Bo Dai, NeurIPS-2017, [lua*]
- Noise-contrastive estimation for answer selection with deep neural networks, Jinfeng Rao, 2016, [torch]
- Improved Deep Metric Learning with Multi-class N-pair Loss Objective, Kihyuk Sohn, NeurIPS-2016, [pytorch]
- Learning word embeddings efficiently with noise-contrastive estimation, Andriy Mnih, NeurIPS-2013,
- Noise-contrastive estimation: A new estimation principle for unnormalized statistical models, Michael Gutmann, AISTATS 2010, [pytorch]
- Dimensionality Reduction by Learning an Invariant Mapping, Raia Hadsell, 2006
- SimCSE: Simple Contrastive Learning of Sentence Embeddings, Tianyu Gao, 2021, [pytorch*]
- ConSERT: A Contrastive Framework for Self-Supervised Sentence Representation Transfer, Yuanmeng Yan, ACL2021, [pytorch*]
- DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations, John Giorgi, ACL2021, [pytorch*]
- Coco-lm: Correcting and contrasting text sequences for language model pretraining, Yu Meng, 2021, [pytorch*]
- Semantic Re-tuning with Contrastive Tension, Fredrik Carlsson, ICLR2021, [Tensorflow*]
- CLEAR: Contrastive Learning for Sentence Representation, Zhuofeng Wu, 2020
- An Unsupervised Sentence Embedding Method by Mutual Information Maximization, Yan Zhang, EMNLP2020, [pytorch*]
- Cert: Contrastive self-supervised learning for language under- standing,
- Efficient estimation of word representations in vector space, Tomas Mikolov, ICLR-Workshop 2013