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Data augmentation is used in machine learning to make the classifier invariant to label-preserving transformations.
“Backpropagation Applied to Handwritten Zip Code Recognition”
Y LeCun, B Boser, J Denker, D Henderson, R Howard, W Hubbard and L Jackel · 1989
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“Face recognition from one example view”
D Beymer and T Poggio · 1995
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“Gradient-based learning applied to document recognition”
Y Lecun, L Bottou, Y Bengio and P Haffner · 1998
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“Incorporating prior information in machine learning by creating virtual examples”
P Niyogi, F Girosi and T Poggio · 1998
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“Better Aggregation in Test-Time Augmentation”
Divya Shanmugam, Davis Blalock, Guha Balakrishnan and John Guttag · 2011
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“Imagenet classification with deep convolutional neural networks”
Alex Krizhevsky, Ilya Sutskever and Geoffrey Hinton · 2012
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“Very deep convolutional networks for large-scale image recognition”
Karen Simonyan and Andrew Zisserman · 2014
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“Unsupervised Visual Representation Learning by Context Prediction”
Carl Doersch, Abhinav Gupta and Alexei Efros · 2015
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“Going deeper with convolutions”
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke and Andrew Rabinovich · 2015
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“Group equivariant convolutional networks”
Taco Cohen and Max Welling · 2016
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“Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles”
Mehdi Noroozi and Paolo Favaro · 2016
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“Regularization with stochastic transformations and perturbations for deep semi-supervised learning”
Mehdi Sajjadi, Mehran Javanmardi and Tolga Tasdizen · 2016
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“Wide Residual Networks”
Sergey Zagoruyko and Nikos Komodakis · 2016
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“Improved Regularization of Convolutional Neural Networks with Cutout”
Terrance DeVries and Graham Taylor · 2017
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“Train longer, generalize better: closing the generalization gap in large batch training of neural networks”
Elad Hoffer, Itay Hubara and Daniel Soudry · 2017
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“Local Group Invariant Representations via Orbit Embeddings”
Anant Raj, Abhishek Kumar, Youssef Mroueh, Tom Fletcher and Bernhard Schoelkopf · 2017
Cited alongside, same era.
“JAX: composable transformations of Python+NumPy programs”, 2018
James Bradbury, Roy Frostig, Peter Hawkins, Matthew Johnson, Chris Leary, Dougal Maclaurin, George Necula, Adam Paszke, Jake VanderPlas, Skye Wanderman-Milne and Qiao Zhang · 2018
Cited alongside, same era.
“Unsupervised Representation Learning by Predicting Image Rotations”
Spyros Gidaris, Praveer Singh and Nikos Komodakis · 2018
Cited alongside, same era.
“Learning invariances using the marginal likelihood”
Mark Wilk, Matthias Bauer, S John and James Hensman · 2018
Cited alongside, same era.
“mixup: Beyond Empirical Risk Minimization”
Hongyi Zhang, Moustapha Cisse, Yann Dauphin and David Lopez-Paz · 2018
Cited alongside, same era.
“AutoAugment: Learning Augmentation Strategies From Data”
“Haiku: Sonnet for JAX”, 2020
Tom Hennigan, Trevor Cai, Tamara Norman and Igor Babuschkin · 2020
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“Augment your batch: Improving generalization through instance repetition”
Elad Hoffer, Tal Ben-Nun, Itay Hubara, Niv Giladi, Torsten Hoefler and Daniel Soudry · 2020
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“Learning Loss for Test-Time Augmentation”
Ildoo Kim, Younghoon Kim and Sungwoong Kim · 2020
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“On the Generalization Benefit of Noise in Stochastic Gradient Descent”
Samuel Smith, Erich Elsen and Soham De · 2020
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“How Good is the Bayes Posterior in Deep Neural Networks Really?”
Florian Wenzel, Kevin Roth, Bastiaan Veeling, Jakub Swiatkowski, Linh Tran, Stephan Mandt, Jasper Snoek, Tim Salimans, Rodolphe Jenatton and Sebastian Nowozin · 2020
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“Unsupervised Data Augmentation for Consistency Training”
Qizhe Xie, Zihang Dai, Eduard Hovy, Thang Luong and Quoc Le · 2020
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Ekin. Cubuk, Barret Zoph, Dandelion Mane, Vijay Vasudevan and Quoc. Le · 2019
Cited alongside, same era.
“Cutmix: Regularization strategy to train strong classifiers with localizable features”
Sangdoo Yun, Dongyoon Han, Seong Oh, Sanghyuk Chun, Junsuk Choe and Youngjoon Yoo · 2019
Cited alongside, same era.
“Unsupervised Learning of Visual Features by Contrasting Cluster Assignments”
Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski and Armand Joulin · 2020
Cited alongside, same era.
“A Simple Framework for Contrastive Learning of Visual Representations”
Ting Chen, Simon Kornblith, Mohammad Norouzi and Geoffrey Hinton · 2020
Cited alongside, same era.
“Big Self-Supervised Models are Strong Semi-Supervised Learners”
Ting Chen, Simon Kornblith, Kevin Swersky, Mohammad Norouzi and Geoffrey Hinton · 2020
Cited alongside, same era.
“RandAugment: Practical Automated Data Augmentation with a Reduced Search Space”
Ekin Cubuk, Barret Zoph, Jon Shlens and Quoc Le · 2020
Cited alongside, same era.
“Batch Normalization Biases Residual Blocks Towards the Identity Function in Deep Networks”
Soham De and Sam Smith · 2020
Cited alongside, same era.
Later among the works it cites.
“Grounding inductive biases in natural images:invariance stems from variations in data”
Diane Bouchacourt, Mark Ibrahim and Ari Morcos · 2021
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“Characterizing signal propagation to close the performance gap in unnormalized ResNets”
Andrew Brock, Soham De and Samuel Smith · 2021
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“High-performance large-scale image recognition without normalization”
Andy Brock, Soham De, Samuel Smith and Karen Simonyan · 2021
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“Drawing Multiple Augmentation Samples Per Image During Training Efficiently Decreases Test Error”
Stanislav Fort, Andrew Brock, Razvan Pascanu, Soham De and Samuel Smith · 2021
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“DAIR: Data Augmented Invariant Regularization”, 2021
Tianjian Huang, Shaunak Halbe, Chinnadhurai Sankar, Pooyan Amini, Satwik Kottur, Alborz Geramifard, Meisam Razaviyayn and Ahmad Beirami · 2021
Later among the works it cites.
“Representation Learning via Invariant Causal Mechanisms”
Jovana Mitrovic, Brian McWilliams, Jacob Walker, Lars Buesing and Charles Blundell · 2021
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“Data augmentation in Bayesian neural networks and the cold posterior effect”
Seth Nabarro, Stoil Ganev, Adrià Garriga-Alonso, Vincent Fortuin, Mark van Wilk and Laurence Aitchison · 2021
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“Going Deeper With Image Transformers”
Hugo Touvron, Matthieu Cord, Alexandre Sablayrolles, Gabriel Synnaeve and Hervé Jégou · 2021
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“Sample Efficiency of Data Augmentation Consistency Regularization”, 2022
Shuo Yang, Yijun Dong, Rachel Ward, Inderjit Dhillon, Sujay Sanghavi and Qi Lei · 2022
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