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Despite the clear performance benefits of data augmentations, little is known about why they are so effective.
Implicit Rugosity Regularization via Data Augmentation
Daniel LeJeune, Randall Balestriero, Hamid Javadi, and Richard G. Baraniuk · 1905
Earlier work this paper cites.
Data Augmentation Revisited: Rethinking the Distribution Gap between Clean and Augmented Data
Zhuoxun He, Lingxi Xie, Xin Chen, Ya Zhang, Yanfeng Wang, and Qi Tian · 1909
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The Effects of Adding Noise During Backpropagation Training on a Generalization Performance
Guozhong An · 1996
Earlier work this paper cites.
Effective training of a neural network character classifier for word recognition
Larry Yaeger, Richard Lyon, and Brandyn Webb · 1996
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Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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SMOTE: Synthetic Minority Over-sampling Technique
N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer · 2002
Earlier work this paper cites.
Affinity and Diversity: Quantifying Mechanisms of Data Augmentation
Raphael Gontijo-Lopes, Sylvia J. Smullin, Ekin D. Cubuk, and Ethan Dyer · 2002
Earlier work this paper cites.
Learning Multiple Layers of Features from Tiny Images
Alex Krizhevsky · 2009
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Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan and Andrew Zisserman · 2014
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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, Alexander C. Berg, and Li Fei-Fei · 2015
Earlier work this paper cites.
Deep Residual Learning for Image Recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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EMNIST: Extending MNIST to handwritten letters
Gregory Cohen, Saeed Afshar, Jonathan Tapson, and André van Schaik · 2017
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Deep Pyramidal Residual Networks
Dongyoon Han, Jiwhan Kim, and Junmo Kim · 2017
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Geometry of Optimization and Implicit Regularization in Deep Learning
Behnam Neyshabur, Ryota Tomioka, Ruslan Salakhutdinov, and Nathan Srebro · 2017
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Revisiting Unreasonable Effectiveness of Data in Deep Learning Era
Chen Sun, Abhinav Shrivastava, Saurabh Singh, and Abhinav Gupta · 2017
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CINIC-10 is not ImageNet or CIFAR-10
Luke N. Darlow, Elliot J. Crowley, Antreas Antoniou, and Amos J. Storkey · 2018
Earlier work this paper cites.
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
Dan Hendrycks and Thomas Dietterich · 2018
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Further Advantages of Data Augmentation on Convolutional Neural Networks
Alex Hernández-García and Peter König · 2018
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Three factors influencing minima in sgd
Stanis\law Jastrzębski, Zachary Kenton, Devansh Arpit, Nicolas Ballas, Asja Fischer, Yoshua Bengio, and Amos Storkey · 2018
Cited alongside, same era.
Visualizing the Loss Landscape of Neural Nets
Hao Li, Zheng Xu, Gavin Taylor, Christoph Studer, and Tom Goldstein · 2018
Cited alongside, same era.
Improving Deep Learning with Generic Data Augmentation
Luke Taylor and Geoff Nitschke · 2018
Cited alongside, same era.
AutoAugment: Learning Augmentation Strategies From Data
Ekin D. Cubuk, Barret Zoph, Dandelion Mane, Vijay Vasudevan, and Quoc V. Le · 2019
Cited alongside, same era.
A Kernel Theory of Modern Data Augmentation
Tri Dao, Albert Gu, Alexander Ratner, Virginia Smith, Chris De Sa, and Christopher Re · 2019
Cited alongside, same era.
Does Data Augmentation Lead to Positive Margin?
Shashank Rajput, Zhili Feng, Zachary Charles, Po-Ling Loh, and Dimitris Papailiopoulos · 2019
Invariance Principle Meets Information Bottleneck for Out-of-Distribution Generalization
Kartik Ahuja, Ethan Caballero, Dinghuai Zhang, Jean-Christophe Gagnon-Audet, Yoshua Bengio, Ioannis Mitliagkas, and Irina Rish · 2021
Later among the works it cites.
Stochastic Training is Not Necessary for Generalization
Jonas Geiping, Micah Goldblum, Phil Pope, Michael Moeller, and Tom Goldstein · 2021
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How Data Augmentation affects Optimization for Linear Regression
Boris Hanin and Yi Sun · 2021
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What Makes Better Augmentation Strategies? Augment Difficult but Not too Different
Jaehyung Kim, Dongyeop Kang, Sungsoo Ahn, and Jinwoo Shin · 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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Cited alongside, same era.
A survey on Image Data Augmentation for Deep Learning
Connor Shorten and Taghi M. Khoshgoftaar · 2019
Cited alongside, same era.
General E(2)-Equivariant Steerable CNNs
Maurice Weiler and Gabriele Cesa · 2019
Cited alongside, same era.
Bridging Theory and Algorithm for Domain Adaptation
Yuchen Zhang, Tianle Liu, Mingsheng Long, and Michael Jordan · 2019
Cited alongside, same era.
Learning Invariances in Neural Networks from Training Data
Gregory Benton, Marc Finzi, Pavel Izmailov, and Andrew G Wilson · 2020
Cited alongside, same era.
A Group-Theoretic Framework for Data Augmentation
Shuxiao Chen, Edgar Dobriban, and Jane Lee · 2020
Cited alongside, same era.
Discovering symbolic models from deep learning with inductive biases
Miles Cranmer, Alvaro Sanchez-Gonzalez, Peter Battaglia, Rui Xu, Kyle Cranmer, David Spergel, and Shirley Ho · 2020
Cited alongside, same era.
Samuel G. Müller and Frank Hutter · 2021
Later among the works it cites.
ResNet strikes back: An improved training procedure in timm
Ross Wightman, Hugo Touvron, and Hervé Jégou · 2021
Later among the works it cites.
Understanding the Generalization Benefit of Model Invariance from a Data Perspective
Sicheng Zhu, Bang An, and Furong Huang · 2021
Later among the works it cites.
PySR: High-Performance Symbolic Regression in Python, November 2022
Miles Cranmer · 2022
Closest in time.
Drawing Multiple Augmentation Samples Per Image During Training Efficiently Decreases Test Error
Stanislav Fort, Andrew Brock, Razvan Pascanu, Soham De, and Samuel L. Smith · 2022
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A Simple Strategy to Provable Invariance via Orbit Mapping
Kanchana Vaishnavi Gandikota, Jonas Geiping, Zorah Lähner, Adam Czapliński, and Michael Moeller · 2022
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On Uncertainty, Tempering, and Data Augmentation in Bayesian Classification
Sanyam Kapoor, Wesley J. Maddox, Pavel Izmailov, and Andrew Gordon Wilson · 2022
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Swin Transformer V2: Scaling Up Capacity and Resolution
Ze Liu, Han Hu, Yutong Lin, Zhuliang Yao, Zhenda Xie, Yixuan Wei, Jia Ning, Yue Cao, Zheng Zhang, Li Dong, Furu Wei, and Baining Guo · 2022
Closest in time.
On the Effects of Artificial Data Modification
Antonia Marcu and Adam Prugel-Bennett · 2022
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How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers
Andreas Peter Steiner, Alexander Kolesnikov, Xiaohua Zhai, Ross Wightman, Jakob Uszkoreit, and Lucas Beyer · 2022
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Asher Trockman and J. Zico Kolter · 2022
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A Comprehensive Survey of Image Augmentation Techniques for Deep Learning
Mingle Xu, Sook Yoon, Alvaro Fuentes, and Dong Sun Park · 2022
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