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While recent automated data augmentation methods lead to state-of-the-art results, their design spaces and the derived data augmentation strategies still incorporate strong human priors.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 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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Deep learning
Ian Goodfellow, Yoshua Bengio, Aaron Courville, and Yoshua Bengio · 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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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 W Taylor · 2017
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Categorical reparameterization with gumbel-softmax
Eric Jang, Shixiang Gu, and Ben Poole · 2017
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Understanding deep learning requires rethinking generalization
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals · 2017
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Adapting auxiliary losses using gradient similarity
Yunshu Du, Wojciech M Czarnecki, Siddhant M Jayakumar, Mehrdad Farajtabar, Razvan Pascanu, and Balaji Lakshminarayanan · 2018
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Data augmentation by pairing samples for images classification
Hiroshi Inoue · 2018
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Darts: Differentiable architecture search
Hanxiao Liu, Karen Simonyan, and Yiming Yang · 2018
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mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz · 2018
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Multigrain: a unified image embedding for classes and instances
Maxim Berman, Hervé Jégou, Andrea Vedaldi, Iasonas Kokkinos, and Matthijs Douze · 2019
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Autoaugment: Learning augmentation strategies from data
Ekin D Cubuk, Barret Zoph, Dandelion Mane, Vijay Vasudevan, and Quoc V Le · 2019
Cited alongside, same era.
Population based augmentation: Efficient learning of augmentation policy schedules
Daniel Ho, Eric Liang, Xi Chen, Ion Stoica, and Pieter Abbeel · 2019
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Fast autoaugment
Sungbin Lim, Ildoo Kim, Taesup Kim, Chiheon Kim, and Sungwoong Kim · 2019
Cited alongside, same era.
Cutmix: Regularization strategy to train strong classifiers with localizable features
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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Differentiable automatic data augmentation
Yonggang Li, Guosheng Hu, Yongtao Wang, Timothy Hospedales, Neil M Robertson, and Yongxin Yang · 2020
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Uniformaugment: A search-free probabilistic data augmentation approach
Tom Ching LingChen, Ava Khonsari, Amirreza Lashkari, Mina Rafi Nazari, Jaspreet Singh Sambee, and Mario A Nascimento · 2020
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Optimizing data usage via differentiable rewards
Xinyi Wang, Hieu Pham, Paul Michel, Antonios Anastasopoulos, Jaime Carbonell, and Graham Neubig · 2020
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Improve unsupervised domain adaptation with mixup training
Shen Yan, Huan Song, Nanxiang Li, Lincan Zou, and Liu Ren · 2020
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Sangdoo Yun, Dongyoon Han, Seong Joon Oh, Sanghyuk Chun, Junsuk Choe, and Youngjoon Yoo · 2019
Cited alongside, same era.
Adversarial autoaugment
Xinyu Zhang, Qiang Wang, Jian Zhang, and Zhao Zhong · 2019
Cited alongside, same era.
A group-theoretic framework for data augmentation
Shuxiao Chen, Edgar Dobriban, and Jane H Lee · 2020
Cited alongside, same era.
Randaugment: Practical automated data augmentation with a reduced search space
Ekin D Cubuk, Barret Zoph, Jonathon Shlens, and Quoc V Le · 2020
Cited alongside, same era.
Faster autoaugment: Learning augmentation strategies using backpropagation
Ryuichiro Hataya, Jan Zdenek, Kazuki Yoshizoe, and Hideki Nakayama · 2020
Cited alongside, same era.
Augmix: A simple data processing method to improve robustness and uncertainty
Dan Hendrycks, Norman Mu, Ekin D Cubuk, Barret Zoph, Justin Gilmer, and Balaji Lakshminarayanan · 2020
Cited alongside, same era.
Stanislav Fort, Andrew Brock, Razvan Pascanu, Soham De, and Samuel L Smith · 2021
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Direct differentiable augmentation search
Aoming Liu, Zehao Huang, Zhiwu Huang, and Naiyan Wang · 2021
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In-loop meta-learning with gradient-alignment reward
Samuel Müller, André Biedenkapp, and Frank Hutter · 2021
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Trivialaugment: Tuning-free yet state-of-the-art data augmentation
Samuel G. Müller and Frank Hutter · 2021
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Resnet strikes back: An improved training procedure in timm
Ross Wightman, Hugo Touvron, and Hervé Jégou · 2021
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