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Existing data augmentation in self-supervised learning, while diverse, fails to preserve the inherent structure of natural images.
Pyramid-based texture analysis/synthesis
David J Heeger and James R Bergen · 1995
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Javier Portilla and Eero P Simoncelli · 2000
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Exploring texture ensembles by efficient Markov chain monte carlo-toward a “trichromacy” theory of texture
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Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories
Li Fei-Fei, Rob Fergus, and Pietro Perona · 2004
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Automated flower classification over a large number of classes
M-E. Nilsback and A. Zisserman · 2008
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Visualizing data using t-sne
Laurens Van der Maaten and Geoffrey Hinton · 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
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3d object representations for fine-grained categorization
Jonathan Krause, Michael Stark, Jia Deng, and Li Fei-Fei · 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
M. Cimpoi, S. Maji, I. Kokkinos, S. Mohamed, and A. Vedaldi · 2014
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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A neural algorithm of artistic style
Leon A Gatys, Alexander S Ecker, and Matthias Bethge · 2015
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Kaggle diabetic retinopathy detection, jul 2015
Kaggle and EyePacs · 2015
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Tensorflow: A system for large-scale machine learning
Martin Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, Manjunath Kudlur, Josh Levenberg, Rajat Monga, Sherry Moore, Derek G. Murray, Benoit Steiner, Paul Tucker, Vijay Vasudevan, Pete Warden, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng · 2016
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Image style transfer using convolutional neural networks
Leon A Gatys, Alexander S Ecker, and Matthias Bethge · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Perceptual losses for real-time style transfer and super-resolution
Justin Johnson, Alexandre Alahi, and Li Fei-Fei · 2016
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Painter by numbers, 2016
Wendy Kan · 2016
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Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
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A learned representation for artistic style
Vincent Dumoulin, Jonathon Shlens, and Manjunath Kudlur · 2017
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Controlling perceptual factors in neural style transfer
Leon A Gatys, Alexander S Ecker, Matthias Bethge, Aaron Hertzmann, and Eli Shechtman · 2017
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Arbitrary style transfer in real-time with adaptive instance normalization
Xun Huang and Serge Belongie · 2017
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Stable and controllable neural texture synthesis and style transfer using histogram losses
Eric Risser, Pierre Wilmot, and Connelly Barnes · 2017
Dynamic instance normalization for arbitrary style transfer
Yongcheng Jing, Xiao Liu, Yukang Ding, Xinchao Wang, Errui Ding, Mingli Song, and Shilei Wen · 2020
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Demystifying contrastive self-supervised learning: Invariances, augmentations and dataset biases
Senthil Purushwalkam and Abhinav Gupta · 2020
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What makes for good views for contrastive learning?
Yonglong Tian, Chen Sun, Ben Poole, Dilip Krishnan, Cordelia Schmid, and Phillip Isola · 2020
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Diversified arbitrary style transfer via deep feature perturbation
Zhizhong Wang, Lei Zhao, Haibo Chen, Lihong Qiu, Qihang Mo, Sihuan Lin, Wei Xing, and Dongming Lu · 2020
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Vicreg: Variance-invariance-covariance regularization for self-supervised learning
Adrien Bardes, Jean Ponce, and Yann LeCun · 2021
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Large batch training of convolutional networks
Yang You, Igor Gitman, and Boris Ginsburg · 2017
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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Robert Geirhos, Patricia Rubisch, Claudio Michaelis, Matthias Bethge, Felix A Wichmann, and Wieland Brendel · 2018
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Optimal whitening and decorrelation
Agnan Kessy, Alex Lewin, and Korbinian Strimmer · 2018
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Avatar-net: Multi-scale zero-shot style transfer by feature decoration
Lu Sheng, Ziyi Lin, Jing Shao, and Xiaogang Wang · 2018
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The unreasonable effectiveness of deep features as a perceptual metric
Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang · 2018
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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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An empirical study of training self-supervised vision transformers
X. Chen, S. Xie, and K. He · 2021
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Exploring simple siamese representation learning
Xinlei Chen and Kaiming He · 2021
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A sliced Wasserstein loss for neural texture synthesis
Eric Heitz, Kenneth Vanhoey, Thomas Chambon, and Laurent Belcour · 2021
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Stylemix: Separating content and style for enhanced data augmentation
Minui Hong, Jinwoo Choi, and Gunhee Kim · 2021
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iNaturalist 2021 competition dataset
iNaturalist 2021 · 2021
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Improving transferability of representations via augmentation-aware self-supervision
Hankook Lee, Kibok Lee, Kimin Lee, Honglak Lee, and Jinwoo Shin · 2021
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Adaattn: Revisit attention mechanism in arbitrary neural style transfer
Songhua Liu, Tianwei Lin, Dongliang He, Fu Li, Meiling Wang, Xin Li, Zhengxing Sun, Qian Li, and Errui Ding · 2021
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Selfaugment: Automatic augmentation policies for self-supervised learning
Colorado J Reed, Sean Metzger, Aravind Srinivas, Trevor Darrell, and Kurt Keutzer · 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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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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Rsa: Reducing semantic shift from aggressive augmentations for self-supervised learning
Yingbin Bai, Erkun Yang, Zhaoqing Wang, Yuxuan Du, Bo Han, Cheng Deng, Dadong Wang, and Tongliang Liu · 2022
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You only cut once: Boosting data augmentation with a single cut
Junlin Han, Pengfei Fang, Weihao Li, Jie Hong, Mohammad Ali Armin, Ian Reid, Lars Petersson, and Hongdong Li · 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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On the importance of hyperparameters and data augmentation for self-supervised learning
Diane Wagner, Fabio Ferreira, Danny Stoll, Robin Tibor Schirrmeister, Samuel Müller, and Frank Hutter · 2022
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