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Contrastive learning methods train visual encoders by comparing views from one instance to others.
Improved baselines with momentum contrastive learning
Xinlei Chen, Haoqi Fan, Ross Girshick, and Kaiming He · 2003
Earlier work this paper cites.
In defense of nearest-neighbor based image classification
Oren Boiman, Eli Shechtman, and Michal Irani · 2008
Earlier work this paper cites.
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Pascal Vincent, Hugo Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol · 2008
Earlier work this paper cites.
Image segmentation using nearest neighbor classifiers based on kernel formation for medical images
R Harini and C Chandrasekar · 2012
Earlier work this paper cites.
Local naive bayes nearest neighbor for image classification
Sancho McCann and David G Lowe · 2012
Earlier work this paper cites.
Frustratingly easy nbnn domain adaptation
Tatiana Tommasi and Barbara Caputo · 2013
Earlier work this paper cites.
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Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
Earlier work this paper cites.
Stochastic backpropagation and variational inference in deep latent gaussian models
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Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
Earlier work this paper cites.
Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
Mask r-cnn
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Earlier work this paper cites.
Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2017
Earlier work this paper cites.
Large batch training of convolutional networks
Yang You, Igor Gitman, and Boris Ginsburg · 2017
Earlier work this paper cites.
Deep learning using rectified linear units (relu)
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Earlier work this paper cites.
Representation learning with contrastive predictive coding
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Earlier work this paper cites.
MMDetection: Open mmlab detection toolbox and benchmark
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Large scale adversarial representation learning
Jeff Donahue and Karen Simonyan · 2019
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Temporal cycle-consistency learning
Debidatta Dwibedi, Yusuf Aytar, Jonathan Tompson, Pierre Sermanet, and Andrew Zisserman · 2019
Cited alongside, same era.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
Cited alongside, same era.
Unsupervised learning of landmarks by descriptor vector exchange
James Thewlis, Samuel Albanie, Hakan Bilen, and Andrea Vedaldi · 2019
Emerging properties in self-supervised vision transformers
Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin · 2021
Later among the works it cites.
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An empirical study of training self-supervised vision transformers
Xinlei Chen, Saining Xie, and Kaiming He · 2021
Later among the works it cites.
An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2021
Later among the works it cites.
With a little help from my friends: Nearest-neighbor contrastive learning of visual representations
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Later among the works it cites.
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Revisiting nearest-neighbor classification for few-shot learning. arxiv 2019
Y Wang, WL Chao, KQ Weinberger, and L Simpleshot Van der Maaten · 2019
Cited alongside, same era.
Pytorch image models
Ross Wightman · 2019
Cited alongside, same era.
Unsupervised learning of visual features by contrasting cluster assignments
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Pooling methods in deep neural networks, a review
Hossein Gholamalinezhad and Hossein Khosravi · 2020
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Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, et al · 2020
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Momentum contrast for unsupervised visual representation learning
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Data-efficient image recognition with contrastive predictive coding
Olivier Henaff · 2020
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Revitalizing cnn attentions via transformers in self-supervised visual representation learning
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Videomoco: Contrastive video representation learning with temporally adversarial examples
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Nearest neighborhood-based deep clustering for source data-absent unsupervised domain adaptation
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Training data-efficient image transformers & distillation through attention
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Self-supervised representation learning with relative predictive coding
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Dense contrastive learning for self-supervised visual pre-training
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Detco: Unsupervised contrastive learning for object detection
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Exploiting the intrinsic neighborhood structure for source-free domain adaptation
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Barlow twins: Self-supervised learning via redundancy reduction
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Context autoencoder for self-supervised representation learning
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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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Videomae: Masked autoencoders are data-efficient learners for self-supervised video pre-training
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