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This paper presents Prototypical Contrastive Learning (PCL), an unsupervised representation learning method that addresses the fundamental limitations of instance-wise contrastive learning.
Improved baselines with momentum contrastive learning
Xinlei Chen, Haoqi Fan, Ross Girshick, and Kaiming He · 2003
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LIBLINEAR: A library for large linear classification
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Fei-Fei Li · 2009
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The pascal visual object classes (VOC) challenge
Mark Everingham, Luc Van Gool, Christopher K. I. Williams, John M. Winn, and Andrew Zisserman · 2010
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Noise-contrastive estimation: A new estimation principle for unnormalized statistical models
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Information theoretic measures for clusterings comparison: Variants, properties, normalization and correction for chance
Xuan Vinh Nguyen, Julien Epps, and James Bailey · 2010
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Discriminative unsupervised feature learning with convolutional neural networks
Alexey Dosovitskiy, Jost Tobias Springenberg, Martin A. Riedmiller, and Thomas Brox · 2014
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Microsoft COCO: common objects in context
Tsung-Yi Lin, Michael Maire, Serge J. Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C. Lawrence Zitnick · 2014
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Mutual information between discrete and continuous data sets
Brian C Ross · 2014
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Learning deep features for scene recognition using places database
Bolei Zhou, Àgata Lapedriza, Jianxiong Xiao, Antonio Torralba, and Aude Oliva · 2014
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Unsupervised visual representation learning by context prediction
Carl Doersch, Abhinav Gupta, and Alexei A. Efros · 2015
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Faster R-CNN: towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross B. Girshick, and Jian Sun · 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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Learning deep parsimonious representations
Renjie Liao, Alexander G. Schwing, Richard S. Zemel, and Raquel Urtasun · 2016
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Unsupervised learning of visual representations by solving jigsaw puzzles
Mehdi Noroozi and Paolo Favaro · 2016
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Context encoders: Feature learning by inpainting
Deepak Pathak, Philipp Krähenbühl, Jeff Donahue, Trevor Darrell, and Alexei A. Efros · 2016
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Unsupervised deep embedding for clustering analysis
Junyuan Xie, Ross B. Girshick, and Ali Farhadi · 2016
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Joint unsupervised learning of deep representations and image clusters
Jianwei Yang, Devi Parikh, and Dhruv Batra · 2016
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Colorful image colorization
Richard Zhang, Phillip Isola, and Alexei A. Efros · 2016
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Deep adaptive image clustering
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Mask R-CNN
Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross B. Girshick · 2017
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Billion-scale similarity search with gpus
Jeff Johnson, Matthijs Douze, and Hervé Jégou · 2017
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Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard S. Zemel · 2017
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Learning deep representations by mutual information estimation and maximization
R. Devon Hjelm, Alex Fedorov, Samuel Lavoie-Marchildon, Karan Grewal, Philip Bachman, Adam Trischler, and Yoshua Bengio · 2019
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Invariant information clustering for unsupervised image classification and segmentation
Xu Ji, João F Henriques, and Andrea Vedaldi · 2019
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Fast autoaugment
Sungbin Lim, Ildoo Kim, Taesup Kim, Chiheon Kim, and Sungwoong Kim · 2019
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Virtual adversarial training: A regularization method for supervised and semi-supervised learning
Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, and Shin Ishii · 2019
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A theoretical analysis of contrastive unsupervised representation learning
Nikunj Saunshi, Orestis Plevrakis, Sanjeev Arora, Mikhail Khodak, and Hrishikesh Khandeparkar · 2019
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Towards k-means-friendly spaces: Simultaneous deep learning and clustering
Bo Yang, Xiao Fu, Nicholas D. Sidiropoulos, and Mingyi Hong · 2017
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Split-brain autoencoders: Unsupervised learning by cross-channel prediction
Richard Zhang, Phillip Isola, and Alexei Efros · 2017
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Deep clustering for unsupervised learning of visual features
Mathilde Caron, Piotr Bojanowski, Armand Joulin, and Matthijs Douze · 2018
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Unsupervised representation learning by predicting image rotations
Spyros Gidaris, Praveer Singh, and Nikos Komodakis · 2018
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Detectron
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Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
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Yonglong Tian, Dilip Krishnan, and Phillip Isola · 2019
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Unsupervised embedding learning via invariant and spreading instance feature
Mang Ye, Xu Zhang, Pong C. Yuen, and Shih-Fu Chang · 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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AET vs. AED: unsupervised representation learning by auto-encoding transformations rather than data
Liheng Zhang, Guo-Jun Qi, Liqiang Wang, and Jiebo Luo · 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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Self-labelling via simultaneous clustering and representation learning
Yuki Markus Asano, Christian Rupprecht, and Andrea Vedaldi · 2020
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Unsupervised learning of visual features by contrasting cluster assignments
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On mutual information maximization for representation learning
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Online deep clustering for unsupervised representation learning
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