Fetching the paper…
Reading the bibliography…
Unsupervised feature learning has made great strides with contrastive learning based on instance discrimination and invariant mapping, as benchmarked on curated class-balanced datasets.
Maximum likelihood from incomplete data via the em algorithm
Arthur P Dempster, Nan M Laird, and Donald B Rubin · 1977
Earlier work this paper cites.
Normalized cuts and image segmentation
Jianbo Shi and Jitendra Malik · 2000
Earlier work this paper cites.
Segmentation with pairwise attraction and repulsion
Stella X. Yu and Jianbo Shi · 2001
Earlier work this paper cites.
Understanding popout through repulsion
Stella X. Yu and Jianbo Shi · 2001
Earlier work this paper cites.
An efficient k-means clustering algorithm: Analysis and implementation
Tapas Kanungo, David M Mount, Nathan S Netanyahu, Christine D Piatko, Ruth Silverman, and Angela Y Wu · 2002
Earlier work this paper cites.
Cluster ensembles—a knowledge reuse framework for combining multiple partitions
Alexander Strehl and Joydeep Ghosh · 2002
Earlier work this paper cites.
Estimation of entropy and mutual information
Liam Paninski · 2003
Earlier work this paper cites.
Multiclass spectral clustering
Stella X. Yu and Jianbo Shi · 2003
Earlier work this paper cites.
Discriminative cluster analysis
Fernando De la Torre and Takeo Kanade · 2006
Earlier work this paper cites.
Dimensionality reduction by learning an invariant mapping
Raia Hadsell, Sumit Chopra, and Yann LeCun · 2006
Earlier work this paper cites.
Adaptive dimension reduction using discriminant analysis and k-means clustering
Chris Ding and Tao Li · 2007
Earlier work this paper cites.
Data clustering: theory, algorithms, and applications
Guojun Gan, Chaoqun Ma, and Jianhong Wu · 2007
Earlier work this paper cites.
A tutorial on spectral clustering
Ulrike Von Luxburg · 2007
Earlier work this paper cites.
Discriminative k-means for clustering
Jieping Ye, Zheng Zhao, and Mingrui Wu · 2008
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
Earlier work this paper cites.
Learning a parametric embedding by preserving local structure
Laurens Van Der Maaten · 2009
Earlier work this paper cites.
Finding dots: Segmentation as popping out regions from boundaries
Elena Bernardis and Stella X. Yu · 2010
Earlier work this paper cites.
The pascal visual object classes (voc) challenge
Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman · 2010
Earlier work this paper cites.
Image clustering using local discriminant models and global integration
Yi Yang, Dong Xu, Feiping Nie, Shuicheng Yan, and Yueting Zhuang · 2010
Earlier work this paper cites.
An analysis of single-layer networks in unsupervised feature learning
Adam Coates, Andrew Ng, and Honglak Lee · 2011
Earlier work this paper cites.
Object detection and segmentation from joint embedding of parts and pixels
Michael Maire, Stella X. Yu, and Pietro Perona · 2011
Earlier work this paper cites.
Spectral embedded clustering: A framework for in-sample and out-of-sample spectral clustering
Feiping Nie, Zinan Zeng, Ivor W Tsang, Dong Xu, and Changshui Zhang · 2011
Earlier work this paper cites.
Spherical k-means clustering
Christian Buchta, Martin Kober, Ingo Feinerer, and Kurt Hornik · 2012
Earlier work this paper cites.
Noise-contrastive estimation of unnormalized statistical models, with applications to natural image statistics
Michael U Gutmann and Aapo Hyvärinen · 2012
Cited alongside, same era.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
Cited alongside, same era.
Learning deep representations for graph clustering
Fei Tian, Bin Gao, Qing Cui, Enhong Chen, and Tie-Yan Liu · 2014
Cited alongside, same era.
Unsupervised visual representation learning by context prediction
Carl Doersch, Abhinav Gupta, and Alexei A Efros · 2015
Cited alongside, same era.
Discriminative unsupervised feature learning with exemplar convolutional neural networks
Alexey Dosovitskiy, Philipp Fischer, Jost Tobias Springenberg, Martin Riedmiller, and Thomas Brox · 2015
Cited alongside, same era.
Multiview rgb-d dataset for object instance detection
Unsupervised pre-training of image features on non-curated data
Mathilde Caron, Piotr Bojanowski, Julien Mairal, and Armand Joulin · 2019
Later among the works it cites.
Scaling and benchmarking self-supervised visual representation learning
Priya Goyal, Dhruv Mahajan, Abhinav Gupta, and Ishan Misra · 2019
Later among the works it cites.
Data-efficient image recognition with contrastive predictive coding
Olivier J Hénaff, Aravind Srinivas, Jeffrey De Fauw, Ali Razavi, Carl Doersch, SM Eslami, and Aaron van den Oord · 2019
Later among the works it cites.
Segsort: Segmentation by discriminative sorting of segments
Jyh-Jing Hwang, Stella X. Yu, Jianbo Shi, Maxwell D Collins, Tien-Ju Yang, Xiao Zhang, and Liang-Chieh Chen · 2019
Later among the works it cites.
Invariant information clustering for unsupervised image classification and segmentation
Xu Ji, João F. Henriques, and Andrea Vedaldi · 2019
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Georgios Georgakis, Md Alimoor Reza, Arsalan Mousavian, Phi-Hung Le, and Jana Košecká · 2016
Cited alongside, same era.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Cited alongside, same era.
Unsupervised learning of visual representations by solving jigsaw puzzles
Mehdi Noroozi and Paolo Favaro · 2016
Cited alongside, same era.
Context encoders: Feature learning by inpainting
Deepak Pathak, Philipp Krahenbuhl, Jeff Donahue, Trevor Darrell, and Alexei A Efros · 2016
Cited alongside, same era.
Unsupervised deep embedding for clustering analysis
Junyuan Xie, Ross Girshick, and Ali Farhadi · 2016
Cited alongside, same era.
Joint unsupervised learning of deep representations and image clusters
Jianwei Yang, Devi Parikh, and Dhruv Batra · 2016
Cited alongside, same era.
Colorful image colorization
Richard Zhang, Phillip Isola, and Alexei A Efros · 2016
Cited alongside, same era.
Later among the works it cites.
Large-scale long-tailed recognition in an open world
Ziwei Liu, Zhongqi Miao, Xiaohang Zhan, Jiayun Wang, Boqing Gong, and Stella X Yu · 2019
Later among the works it cites.
Self-supervised learning of pretext-invariant representations
Ishan Misra and Laurens van der Maaten · 2019
Later among the works it cites.
On variational bounds of mutual information
Ben Poole, Sherjil Ozair, Aaron van den Oord, Alexander A Alemi, and George Tucker · 2019
Later among the works it cites.
Yonglong Tian, Dilip Krishnan, and Phillip Isola · 2019
Later among the works it cites.
On mutual information maximization for representation learning
Michael Tschannen, Josip Djolonga, Paul K Rubenstein, Sylvain Gelly, and Mario Lucic · 2019
Later among the works it cites.
Unsupervised embedding learning via invariant and spreading instance feature
Mang Ye, Xu Zhang, Pong C Yuen, and Shih-Fu Chang · 2019
Later among the works it cites.
S4l: Self-supervised semi-supervised learning
Xiaohua Zhai, Avital Oliver, Alexander Kolesnikov, and Lucas Beyer · 2019
Later among the works it cites.
Self-supervised learning via conditional motion propagation
Xiaohang Zhan, Xingang Pan, Ziwei Liu, Dahua Lin, and Chen Change Loy · 2019
Later among the works it cites.
Local aggregation for unsupervised learning of visual embeddings
Chengxu Zhuang, Alex Lin Zhai, Daniel Yamins, , et al · 2019
Later among the works it cites.
Unsupervised learning of visual features by contrasting cluster assignments
Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin · 2020
Closest in time.
A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
Closest in time.
Improved baselines with momentum contrastive learning
Xinlei Chen, Haoqi Fan, Ross Girshick, and Kaiming He · 2020
Closest in time.
Exploring simple siamese representation learning
Xinlei Chen and Kaiming He · 2020
Closest in time.
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
Closest in time.
Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2020
Closest in time.
Prototypical contrastive learning of unsupervised representations
Junnan Li, Pan Zhou, Caiming Xiong, Richard Socher, and Steven CH Hoi · 2020
Closest in time.
What makes for good views for contrastive learning
Yonglong Tian, Chen Sun, Ben Poole, Dilip Krishnan, Cordelia Schmid, and Phillip Isola · 2020
Closest in time.
OpenSelfSup: Open mmlab self-supervised learning toolbox and benchmark
Xiaohang Zhan, Jiahao Xie, Ziwei Liu, Dahua Lin, and Chen Change Loy · 2020
Closest in time.