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Data augmentation is an effective technique to improve the generalization of deep neural networks.
Maximum likelihood from incomplete data via the em algorithm
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Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L. Li, K. Li, and L. Fei-Fei · 2009
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Ensemble of exemplar-svms for object detection and beyond
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Imagenet classification with deep convolutional neural networks
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Self-paced learning with diversity
L. Jiang, D. Meng, S. Yu, Z. Lan, S. Shan, and A. Hauptmann · 2014
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The cifar-10 dataset
A. Krizhevsky, V. Nair, and G. Hinton · 2014
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Stochastic gradient descent, weighted sampling, and the randomized kaczmarz algorithm
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Sequence to sequence learning with neural networks
I. Sutskever, O. Vinyals, and Q. V. Le · 2014
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Accelerating minibatch stochastic gradient descent using stratified sampling
P. Zhao and T. Zhang · 2014
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That’s so annoying!!!: A lexical and frame-semantic embedding based data augmentation approach to automatic categorization of annoying behaviors using# petpeeve tweets
D. Wang, W. Y.and Yang · 2015
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Character-level convolutional networks for text classification
X. Zhang, J. Zhao, and Y. LeCun · 2015
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Deep learning , volume 1
I. Goodfellow, Y. Bengio, and A. Courville · 2016
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Improved regularization of convolutional neural networks with cutout
T. DeVries and G. W. Taylor · 2017
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Focal loss for dense object detection
T. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár · 2017
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A bayesian data augmentation approach for learning deep models
T. Tran, T. Pham, G. Carneiro, L. Palmer, and I. Reid · 2017
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Attention is all you need
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N Gomez, Ł. Kaiser, and I. Polosukhin · 2017
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Data noising as smoothing in neural network language models
Z. Xie, S. I. Wang, J. Li, D. Lévy, A. Nie, D. Jurafsky, and A. Y. Ng · 2017
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Importance sampling for minibatches
D. Csiba and P. Richtárik · 2018
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Autoaugment: Learning augmentation policies from data
E. D. Cubuk, B. Zoph, D. Mane, V. Vasudevan, and Q. V. Le · 2018
Robust neural machine translation with doubly adversarial inputs
Y. Cheng, L. Jiang, and W. Macherey · 2019
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Bert: Pre-training of deep bidirectional transformers for language understanding
J. Devlin, M. Chang, K. Lee, and K. Toutanova · 2019
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Augmenting data with mixup for sentence classification: An empirical study
H. Guo, Y. Mao, and R. Zhang · 2019
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Tinybert: Distilling bert for natural language understanding
X. Jiao, Y. Yin, L. Shang, X. Jiang, X. Chen, L. Li, F. Wang, and Q. Liu · 2019
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Fast autoaugment
S. Lim, I. Kim, T. Kim, C. Kim, and S. Kim · 2019
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Cited alongside, same era.
Focal loss for dense object detection
P. Goyal and K. He · 2018
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Mentornet: Learning data-driven curriculum for very deep neural networks on corrupted labels
L. Jiang, Z. Zhou, T. Leung, L. Li, and F. Li · 2018
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Not all samples are created equal: Deep learning with importance sampling
A. Katharopoulos and F. Fleuret · 2018
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A game-theoretic adversarial approach to dynamic network prediction
J. Li, B. Ziebart, and B. Berger-Wolf · 2018
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Decoupled weight decay regularization
I. Loshchilov and F. Hutter · 2018
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Towards deep learning models resistant to adversarial attacks
A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu · 2018
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J. Martens · 2019
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Specaugment: A simple data augmentation method for automatic speech recognition
D. S. Park, W. Chan, Y. Zhang, C. Chiu, B. Zoph, E. D. Cubuk, and Q. V. Le · 2019
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Language models are unsupervised multitask learners
A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, and I. Sutskever · 2019
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Adversarial training can hurt generalization
A. Raghunathan, S. M. Xie, F. Yang, J. C Duchi, and P. Liang · 2019
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Meta-weight-net: Learning an explicit mapping for sample weighting
J. Shu, Q. Xie, L. Yi, Q. Zhao, S. Zhou, Z. Xu, and D. Meng · 2019
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Glue: A multi-task benchmark and analysis platform for natural language understanding
A. Wang, A. Singh, J. Michael, F. Hill, O. Levy, and S. R. Bowman · 2019
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Eda: Easy data augmentation techniques for boosting performance on text classification tasks
J. Wei and K. Zou · 2019
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Unsupervised data augmentation for consistency training
Q. Xie, Z. Dai, E. Hovy, M. Luong, and Q. V. Le · 2019
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Advaug: Robust adversarial augmentation for neural machine translation
Y. Cheng, L. Jiang, W. Macherey, and J. Eisenstein · 2020
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Data augmentation using pre-trained transformer models
V. Kumar, A. Choudhary, and E. Cho · 2020
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Freelb: Enhanced adversarial training for natural language understanding
C. Zhu, Y. Cheng, Z. Gan, S. Sun, T. Goldstein, and J. Liu · 2020
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