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Data augmentation (DA) is a powerful workhorse for bolstering performance in modern machine learning.
“Training with noise is equivalent to Tikhonov regularization”
Chris Bishop · 1995
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“Vicinal risk minimization”
Olivier Chapelle, Jason Weston, Léon Bottou and Vladimir Vapnik · 2001
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“Learning with kernels: support vector machines, regularization, optimization, and beyond”
Bernhard Schölkopf and Alexander Smola · 2002
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“The elements of statistical learning: data mining, inference, and prediction”
Trevor Hastie, Robert Tibshirani, Jerome Friedman and Jerome Friedman · 2009
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“Introduction to the non-asymptotic analysis of random matrices”
Roman Vershynin · 2010
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“User-friendly tail bounds for sums of random matrices”
Joel Tropp · 2012
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“Invariant scattering convolution networks”
Joan Bruna and Stéphane Mallat · 2013
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“Dropout as data augmentation”
Xavier Bouthillier, Kishore Konda, Pascal Vincent and Roland Memisevic · 2015
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“Dropout as data augmentation”
Kishore Konda, Xavier Bouthillier, Roland Memisevic and Pascal Vincent · 2015
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“Learning with Group Invariant Features: A Kernel Perspective.”
Youssef Mroueh, Stephen Voinea and Tomaso Poggio · 2015
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“Group equivariant convolutional networks”
Taco Cohen and Max Welling · 2016
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“Improved regularization of convolutional neural networks with cutout”
Terrance DeVries and Graham Taylor · 2017
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Guoliang Kang, Xuanyi Dong, Liang Zheng and Yi Yang · 2017
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“Local group invariant representations via orbit embeddings”
Anant Raj, Abhishek Kumar, Youssef Mroueh, Tom Fletcher and Bernhard Schölkopf · 2017
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“Learning to compose domain-specific transformations for data augmentation”
Alexander Ratner, Henry Ehrenberg, Zeshan Hussain, Jared Dunnmon and Christopher Ré · 2017
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“mixup: Beyond empirical risk minimization”
Hongyi Zhang, Moustapha Cisse, Yann Dauphin and David Lopez-Paz · 2017
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“Panning for gold:‘model-X’knockoffs for high dimensional controlled variable selection”
Emmanuel Candes, Yingying Fan, Lucas Janson and Jinchi Lv · 2018
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“Dropout as a low-rank regularizer for matrix factorization”
Jacopo Cavazza, Pietro Morerio, Benjamin Haeffele, Connor Lane, Vittorio Murino and Rene Vidal · 2018
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“Unsupervised representation learning by predicting image rotations”
Spyros Gidaris, Praveer Singh and Nikos Komodakis · 2018
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“Dealing with bias via data augmentation in supervised learning scenarios”
Vasileios Iosifidis and Eirini Ntoutsi · 2018
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“Neural tangent kernel: Convergence and generalization in neural networks”
Arthur Jacot, Franck Gabriel and Clément Hongler · 2018
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“On the implicit bias of dropout”
Poorya Mianjy, Raman Arora and Rene Vidal · 2018
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“Reconciling modern machine-learning practice and the classical bias–variance trade-off”
Mikhail Belkin, Daniel Hsu, Siyuan Ma and Soumik Mandal · 2019
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“Practical data augmentation with no separate search”
Ekin Cubuk, Barret Zoph, Jonathon Shlens and Quoc Le · 2019
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“A kernel theory of modern data augmentation”
Tri Dao, Albert Gu, Alexander Ratner, Virginia Smith, Chris De and Christopher Ré · 2019
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“Surprises in high-dimensional ridgeless least squares interpolation”
Trevor Hastie, Andrea Montanari, Saharon Rosset and Ryan Tibshirani · 2019
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“Implicit rugosity regularization via data augmentation”
Daniel LeJeune, Randall Balestriero, Hamid Javadi and Richard Baraniuk · 2019
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“Enhanced convolutional neural tangent kernels”
Zhiyuan Li, Ruosong Wang, Dingli Yu, Simon Du, Wei Hu, Ruslan Salakhutdinov and Sanjeev Arora · 2019
Cited alongside, same era.
Andrea Montanari, Feng Ruan, Youngtak Sohn and Jun Yan · 2019
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“A survey on image data augmentation for deep learning”
Connor Shorten and Taghi Khoshgoftaar · 2019
Cited alongside, same era.
“Invariance-inducing regularization using worst-case transformations suffices to boost accuracy and spatial robustness”
Fanny Yang, Zuowen Wang and Christina Heinze-Deml · 2019
Cited alongside, same era.
“Towards understanding ensemble, knowledge distillation and self-distillation in deep learning”
“A survey of data augmentation approaches for NLP”
Steven Feng, Varun Gangal, Jason Wei, Sarath Chandar, Soroush Vosoughi, Teruko Mitamura and Eduard Hovy · 2021
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“How data augmentation affects optimization for linear regression”
Boris Hanin and Yi Sun · 2021
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“Drop, swap, and generate: A self-supervised approach for generating neural activity”
Ran Liu, Mehdi Azabou, Max Dabagia, Chi-Heng Lin, Mohammad Gheshlaghi, Keith Hengen, Michal Valko and Eva Dyer · 2021
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“Adaptive data augmentation for supervised learning over missing data”
Tongyu Liu, Ju Fan, Yinqing Luo, Nan Tang, Guoliang Li and Xiaoyong Du · 2021
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“Learning with invariances in random features and kernel models”
Song Mei, Theodor Misiakiewicz and Andrea Montanari · 2021
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Zeyuan Allen-Zhu and Yuanzhi Li · 2020
Cited alongside, same era.
“Benign overfitting in linear regression”
Peter Bartlett, Philip Long, Gábor Lugosi and Alexander Tsigler · 2020
Cited alongside, same era.
“Two models of double descent for weak features”
Mikhail Belkin, Daniel Hsu and Ji Xu · 2020
Cited alongside, same era.
“A group-theoretic framework for data augmentation”
Shuxiao Chen, Edgar Dobriban and Jane Lee · 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.
“Affinity and diversity: Quantifying mechanisms of data augmentation”
Raphael Gontijo-Lopes, Sylvia Smullin, Ekin Cubuk and Ethan Dyer · 2020
Cited alongside, same era.
“Bootstrap your own latent-a new approach to self-supervised learning”
Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila, Zhaohan Guo and Mohammad Gheshlaghi · 2020
Cited alongside, same era.
Like Hui and Mikhail Belkin · 2020
Cited alongside, same era.
“Classification vs regression in overparameterized regimes: Does the loss function matter?”
Vidya Muthukumar, Adhyyan Narang, Vignesh Subramanian, Mikhail Belkin, Daniel Hsu and Anant Sahai · 2021
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“Uniform consistency of cross-validation estimators for high-dimensional ridge regression”
Pratik Patil, Yuting Wei, Alessandro Rinaldo and Ryan Tibshirani · 2021
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Dominic Richards, Edgar Dobriban and Patrick Rebeschini · 2021
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Abhishek Sinha, Kumar Ayush, Jiaming Song, Burak Uzkent, Hongxia Jin and Stefano Ermon · 2021
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Ke Wang and Christos Thrampoulidis · 2021
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“A Simple Baseline for Semi-supervised Semantic Segmentation with Strong Data Augmentation”
Jianlong Yuan, Yifan Liu, Chunhua Shen, Zhibin Wang and Hao Li · 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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“Understanding deep learning (still) requires rethinking generalization”
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht and Oriol Vinyals · 2021
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“Benign overfitting of constant-stepsize sgd for linear regression”
Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu and Sham Kakade · 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, Michael Rabbat and Nicolas Ballas · 2022
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“The effects of regularization and data augmentation are class dependent”
Randall Balestriero, Leon Bottou and Yann LeCun · 2022
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Randall Balestriero, Ishan Misra and Yann LeCun · 2022
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“Boosting Robustness of Image Matting with Context Assembling and Strong Data Augmentation”
Yutong Dai, Brian Price, He Zhang and Chunhua Shen · 2022
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“A model of double descent for high-dimensional binary linear classification”
Zeyu Deng, Abla Kammoun and Christos Thrampoulidis · 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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“Harmless interpolation in regression and classification with structured features”
Andrew McRae, Santhosh Karnik, Mark Davenport and Vidya Muthukumar · 2022
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“Mitigating multiple descents: A model-agnostic framework for risk monotonization”
Pratik Patil, Arun Kuchibhotla, Yuting Wei and Alessandro Rinaldo · 2022
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“Open-Set Fault Diagnosis via Supervised Contrastive Learning with Negative Out-of-Distribution Data Augmentation”
Peng Peng, Jiaxun Lu, Tingyu Xie, Shuting Tao, Hongwei Wang and Heming Zhang · 2022
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“The Implicit Bias of Benign Overfitting”
Ohad Shamir · 2022
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“Data Augmentation as Feature Manipulation: a story of desert cows and grass cows”
Ruoqi Shen, Sébastien Bubeck and Suriya Gunasekar · 2022
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“The ssl interplay: Augmentations, inductive bias, and generalization”
Vivien Cabannes, Bobak Kiani, Randall Balestriero, Yann LeCun and Alberto Bietti · 2023
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