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Data augmentation has been an indispensable tool to improve the performance of deep neural networks, however the augmentation can hardly transfer among different tasks and datasets.
ImageNet
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, Geoffrey Hinton, et al · 2009
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Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y. Ng · 2011
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Microsoft
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Categorical reparameterization with
Eric Jang, Shixiang Gu, and Ben Poole · 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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Xavier Gastaldi · 2017
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Focal loss for dense object detection
Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár · 2017
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Neural architecture search with reinforcement learning
Barret Zoph and Quoc V Le · 2017
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Backpropagation through the void: Optimizing control variates for black-box gradient estimation
Will Grathwohl, Dami Choi, Yuhuai Wu, Geoff Roeder, and David Duvenaud · 2018
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Data augmentation by pairing samples for images classification
Hiroshi Inoue · 2018
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Learning to reweight examples for robust deep learning
Mengye Ren, Wenyuan Zeng, Bin Yang, and Raquel Urtasun · 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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David Berthelot, Nicholas Carlini, Ekin D Cubuk, Alex Kurakin, Kihyuk Sohn, Han Zhang, and Colin Raffel · 2019
Cited alongside, same era.
MixMatch
David Berthelot, Nicholas Carlini, Ian Goodfellow, Nicolas Papernot, Avital Oliver, and Colin A Raffel · 2019
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Stabilizing DARTS with amended gradient estimation on architectural parameters
Kaifeng Bi, Changping Hu, Lingxi Xie, Xin Chen, Longhui Wei, and Qi Tian · 2019
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MMDetection: Open mmlab detection toolbox and benchmark
Kai Chen, Jiaqi Wang, Jiangmiao Pang, Yuhang Cao, Yu Xiong, Xiaoxiao Li, Shuyang Sun, Wansen Feng, Ziwei Liu, Jiarui Xu, Zheng Zhang, Dazhi Cheng, Chenchen Zhu, Tianheng Cheng, Qijie Zhao, Buyu Li, Xin Lu, Rui Zhu, Yue Wu, Jifeng Dai, Jingdong Wang, Jianping Shi, Wanli Ouyang, Chen Change Loy, and Dahua Lin · 2019
Cited alongside, same era.
AutoAugment
Ekin D Cubuk, Barret Zoph, Dandelion Mane, Vijay Vasudevan, and Quoc V Le · 2019
Meta approach to data augmentation optimization
Ryuichiro Hataya, Jan Zdenek, Kazuki Yoshizoe, and Hideki Nakayama · 2020
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Image augmentation is all you need: Regularizing deep reinforcement learning from pixels
Ilya Kostrikov, Denis Yarats, and Rob Fergus · 2020
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PointAugment
Ruihui Li, Xianzhi Li, Pheng-Ann Heng, and Chi-Wing Fu · 2020
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DADA: differentiable automatic data augmentation
Yonggang Li, Guosheng Hu, Yongtao Wang, Timothy M. Hospedales, Neil Martin Robertson, and Yongxin Yang · 2020
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Learning data augmentation with online bilevel optimization for image classification
Saypraseuth Mounsaveng, Issam Laradji, Ismail Ben Ayed, David Vazquez, and Marco Pedersoli · 2020
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Cited alongside, same era.
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 · 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
Cited alongside, same era.
Online hyper-parameter learning for auto-augmentation strategy
Chen Lin, Minghao Guo, Chuming Li, Xin Yuan, Wei Wu, Junjie Yan, Dahua Lin, and Wanli Ouyang · 2019
Cited alongside, same era.
DARTS: Differentiable architecture search
Hanxiao Liu, Karen Simonyan, and Yiming Yang · 2019
Cited alongside, same era.
Unsupervised data augmentation for consistency training
Qizhe Xie, Zihang Dai, E. Hovy, Minh-Thang Luong, and Quoc V. Le · 2019
Cited alongside, same era.
Hypernetwork-based augmentation
Chih-Yang Chen, Che-Han Chang, and Edward Y Chang · 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.
OnlineAugment
Zhiqiang Tang, Yunhe Gao, Leonid Karlinsky, Prasanna Sattigeri, Rogerio Feris, and Dimitris Metaxas · 2020
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Improving
Keyu Tian, Chen Lin, Ming Sun, Luping Zhou, Junjie Yan, and Wanli Ouyang · 2020
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On the generalization effects of linear transformations in data augmentation
Sen Wu, Hongyang R Zhang, Gregory Valiant, and Christopher Ré · 2020
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Adversarial examples improve image recognition
Cihang Xie, Mingxing Tan, Boqing Gong, Jiang Wang, Alan L. Yuille, and Quoc V. Le · 2020
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Adversarial
Xinyu Zhang, Qiang Wang, Jian Zhang, and Zhao Zhong · 2020
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Differentiable augmentation for data-efficient gan training
Shengyu Zhao, Zhijian Liu, Ji Lin, Jun-Yan Zhu, and Song Han · 2020
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Data augmentation with mobius transformations
Sharon Zhou, Jiequan Zhang, Hang Jiang, Torbjörn Lundh, and Andrew Y Ng · 2020
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Learning data augmentation strategies for object detection
Barret Zoph, Ekin D Cubuk, Golnaz Ghiasi, Tsung-Yi Lin, Jonathon Shlens, and Quoc V Le · 2020
Later among the works it cites.
Metaaugment: Sample-aware data augmentation policy learning, 2021
Fengwei Zhou, Jiawei Li, Chuanlong Xie, Fei Chen, Lanqing Hong, Rui Sun, and Zhenguo Li · 2021
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