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Self-distillation methods using Siamese networks are popular for self-supervised pre-training.
A stochastic approximation type em algorithm for the mixture problem
Gilles Celeux and Jean Diebolt · 1992
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Signature verification using a" siamese" time delay neural network
Jane Bromley, Isabelle Guyon, Yann LeCun, Eduard Säckinger, and Roopak Shah · 1993
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Convergence of a stochastic approximation version of the em algorithm
Bernard Delyon, Marc Lavielle, and Eric Moulines · 1999
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Improved baselines with momentum contrastive learning
Xinlei Chen, Haoqi Fan, Ross Girshick, and Kaiming He · 2003
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Clustering on the unit hypersphere using von Mises-Fisher distributions
Arindam Banerjee, Inderjit S Dhillon, Joydeep Ghosh, Suvrit Sra, and Greg Ridgeway · 2005
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Object retrieval with large vocabularies and fast spatial matching
James Philbin, Ondrej Chum, Michael Isard, Josef Sivic, and Andrew Zisserman · 2007
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Automated flower classification over a large number of classes
Maria-Elena Nilsback and Andrew Zisserman · 2008
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Lost in quantization: Improving particular object retrieval in large scale image databases
James Philbin, Ondrej Chum, Michael Isard, Josef Sivic, and Andrew Zisserman · 2008
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
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Sun database: Large-scale scene recognition from abbey to zoo
Jianxiong Xiao, James Hays, Krista A Ehinger, Aude Oliva, and Antonio Torralba · 2010
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Cats and dogs
Omkar M Parkhi, Andrea Vedaldi, Andrew Zisserman, and CV Jawahar · 2012
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Fine-grained visual classification of aircraft
Subhransu Maji, Esa Rahtu, Juho Kannala, Matthew Blaschko, and Andrea Vedaldi · 2013
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Food-101 – mining discriminative components with random forests
Lukas Bossard, Matthieu Guillaumin, and Luc Van Gool · 2014
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Describing textures in the wild
Mircea Cimpoi, Subhransu Maji, Iasonas Kokkinos, Sammy Mohamed, and Andrea Vedaldi · 2014
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von Mises-Fisher clustering models
Siddharth Gopal and Yiming Yang · 2014
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Bayesian estimation of the von-Mises Fisher mixture model with variational inference
Jalil Taghia, Zhanyu Ma, and Arne Leijon · 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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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Learning representations for automatic colorization
Gustav Larsson, Michael Maire, and Gregory Shakhnarovich · 2016
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Unsupervised learning of visual representations by solving jigsaw puzzles
Mehdi Noroozi and Paolo Favaro · 2016
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Weight normalization: A simple reparameterization to accelerate training of deep neural networks
Tim Salimans and Durk P Kingma · 2016
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Colorful image colorization
Richard Zhang, Phillip Isola, and Alexei A Efros · 2016
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von Mises-Fisher mixture model-based deep learning: Application to face verification
Md Hasnat, Julien Bohné, Jonathan Milgram, Stéphane Gentric, Liming Chen, et al · 2017
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The 2017 davis challenge on video object segmentation
Jordi Pont-Tuset, Federico Perazzi, Sergi Caelles, Pablo Arbeláez, Alexander Sorkine-Hornung, and Luc Van Gool · 2017
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Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
Antti Tarvainen and Harri Valpola · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Deep clustering for unsupervised learning of visual features
Mathilde Caron, Piotr Bojanowski, Armand Joulin, and Matthijs Douze · 2018
Semi-supervised learning of visual features by non-parametrically predicting view assignments with support samples
Mahmoud Assran, Mathilde Caron, Ishan Misra, Piotr Bojanowski, Armand Joulin, Nicolas Ballas, and Michael Rabbat · 2021
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Beit: Bert pre-training of image transformers
Hangbo Bao, Li Dong, Songhao Piao, and Furu Wei · 2021
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Sparse mixture of von Mises-Fisher distribution
Florian Barbaro and Fabrice Rossi · 2021
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Emerging properties in self-supervised vision transformers
Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin · 2021
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Exploring simple siamese representation learning
Xinlei Chen and Kaiming He · 2021
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An empirical study of training self-supervised vision transformers
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Learning image representations by completing damaged jigsaw puzzles
Dahun Kim, Donghyeon Cho, Donggeun Yoo, and In So Kweon · 2018
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Unsupervised representation learning by predicting image rotations
Nikos Komodakis and Spyros Gidaris · 2018
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Fixing weight decay regularization in adam
Ilya Loshchilov and Frank Hutter · 2018
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Unsupervised feature learning via non-parametric instance discrimination
Zhirong Wu, Yuanjun Xiong, Stella X Yu, and Dahua Lin · 2018
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Unsupervised pre-training of image features on non-curated data
Mathilde Caron, Piotr Bojanowski, Julien Mairal, and Armand Joulin · 2019
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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
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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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Xinlei Chen, Saining Xie, and Kaiming He · 2021
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How well do self-supervised models transfer?
Linus Ericsson, Henry Gouk, and Timothy M Hospedales · 2021
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Whitening for self-supervised representation learning
Aleksandr Ermolov, Aliaksandr Siarohin, Enver Sangineto, and Nicu Sebe · 2021
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Compressive visual representations
Kuang-Huei Lee, Anurag Arnab, Sergio Guadarrama, John Canny, and Ian Fischer · 2021
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Efficient self-supervised vision transformers for representation learning
Chunyuan Li, Jianwei Yang, Pengchuan Zhang, Mei Gao, Bin Xiao, Xiyang Dai, Lu Yuan, and Jianfeng Gao · 2021
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Swin transformer: Hierarchical vision transformer using shifted windows
Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo · 2021
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Training data-efficient image transformers & distillation through attention
Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, and Hervé Jégou · 2021
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Self-supervised learning with swin transformers
Zhenda Xie, Yutong Lin, Zhuliang Yao, Zheng Zhang, Qi Dai, Yue Cao, and Han Hu · 2021
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Barlow twins: Self-supervised learning via redundancy reduction
Jure Zbontar, Li Jing, Ishan Misra, Yann LeCun, and Stéphane Deny · 2021
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Image bert pre-training with online tokenizer
Jinghao Zhou, Chen Wei, Huiyu Wang, Wei Shen, Cihang Xie, Alan Yuille, and Tao Kong · 2021
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Masked siamese networks for label-efficient learning
Mahmoud Assran, Mathilde Caron, Ishan Misra, Piotr Bojanowski, Florian Bordes, Pascal Vincent, Armand Joulin, Mike Rabbat, and Nicolas Ballas · 2022
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NIST Digital Library of Mathematical Functions
DLMF · 2022
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Masked autoencoders are scalable vision learners
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick · 2022
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Caltech 101, 2022
Fei-Fei Li, Marco Andreeto, Marc’Aurelio Ranzato, and Pietro Perona · 2022
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Beit v2: Masked image modeling with vector-quantized visual tokenizers
Zhiliang Peng, Li Dong, Hangbo Bao, Qixiang Ye, and Furu Wei · 2022
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What do we maximize in self-supervised learning?
Ravid Shwartz-Ziv, Randall Balestriero, and Yann LeCun · 2022
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Deit iii: Revenge of the vit
Hugo Touvron, Matthieu Cord, and Hervé Jégou · 2022
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