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Data augmentation is a critical component of training deep learning models.
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Data augmentation generative adversarial networks
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Dataset augmentation in feature space
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Random erasing data augmentation
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Squeeze-and-excitation networks
J. Hu, L. Shen, and G. Sun · 2017
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Speed/accuracy trade-offs for modern convolutional object detectors
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Smart augmentation learning an optimal data augmentation strategy
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Focal loss for dense object detection
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Progressive neural architecture search
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Generative adversarial network based acoustic scene training set augmentation and selection using svm hyper-plane
S. Mun, S. Park, D. K. Han, and H. Ko · 2017
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E. D. Cubuk, B. Zoph, D. Mane, V. Vasudevan, and Q. V. Le · 2018
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DropBlock: A regularization method for convolutional networks
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Detectron, 2018
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Megdet: A large mini-batch object detector
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Manifold mixup: Encouraging meaningful on-manifold interpolation as a regularizer
V. Verma, A. Lamb, C. Beckham, A. Courville, I. Mitliagkis, and Y. Bengio · 2018
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Metaanchor: Learning to detect objects with customized anchors
T. Yang, X. Zhang, Z. Li, W. Zhang, and J. Sun · 2018
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Adversarial examples are a natural consequence of test error in noise
N. Ford, J. Gilmer, N. Carlini, and D. Cubuk · 2019
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NAS-FPN: Learning scalable feature pyramid architecture for object detection
G. Ghiasi, T.-Y. Lin, R. Pang, and Q. V. Le · 2019
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Population based augmentation: Efficient learning of augmentation policy schedules
D. Ho, E. Liang, I. Stoica, P. Abbeel, and X. Chen · 2019
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S. Lim, I. Kim, T. Kim, C. Kim, and S. Kim · 2019
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Improving robustness without sacrificing accuracy with patch gaussian augmentation
R. G. Lopes, D. Yin, B. Poole, J. Gilmer, and E. D. Cubuk · 2019
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Regularized evolution for image classifier architecture search
E. Real, A. Aggarwal, Y. Huang, and Q. V. Le · 2019
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A fourier perspective on model robustness in computer vision
D. Yin, R. G. Lopes, J. Shlens, E. D. Cubuk, and J. Gilmer · 2019
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