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Data augmentation is an effective technique for improving the accuracy of modern image classifiers.
Neocognitron: A self-organizing neural network model for a mechanism of visual pattern recognition
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Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories
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Automated flower classification over a large number of classes
M.-E. Nilsback and A. Zisserman · 2008
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Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
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What is the best multi-stage architecture for object recognition?
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Learning multiple layers of features from tiny images
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Random search for hyper-parameter optimization
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Multi-column deep neural networks for image classification
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Imagenet classification with deep convolutional neural networks
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Some improvements on deep convolutional neural network based image classification
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Collecting a large-scale dataset of fine-grained cars
J. Krause, J. Deng, M. Stark, and L. Fei-Fei · 2013
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Fine-grained visual classification of aircraft
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Regularization of neural networks using dropconnect
L. Wan, M. Zeiler, S. Zhang, Y. Le Cun, and R. Fergus · 2013
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Generative adversarial nets
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Apac: Augmented pattern classification with neural networks
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Very deep convolutional networks for large-scale image recognition
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Going deeper with convolutions
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Tensorflow: A system for large-scale machine learning
M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, M. Kudlur, J. Levenberg, R. Monga, S. Moore, D. G. Murray, B. Steiner, P. Tucker, V. Vasudevan, P. Warden, M. Wicke, Y. Yu, and X. Zheng · 2016
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Deep residual learning for image recognition
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Temporal ensembling for semi-supervised learning
S. Laine and T. Aila · 2016
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SGDR: Stochastic gradient descent with warm restarts
I. Loshchilov and F. Hutter · 2016
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Virtual adversarial training: a regularization method for supervised and semi-supervised learning
T. Miyato, S.-i. Maeda, M. Koyama, and S. Ishii · 2016
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Regularization with stochastic transformations and perturbations for deep semi-supervised learning
M. Sajjadi, M. Javanmardi, and T. Tasdizen · 2016
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Rendergan: Generating realistic labeled data
L. Sixt, B. Wild, and T. Landgraf · 2016
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Proximal policy optimization algorithms
J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov · 2017
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Inception-v4, inception-resnet and the impact of residual connections on learning
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Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
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A bayesian data augmentation approach for learning deep models
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Genetic CNN
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Aggregated residual transformations for deep neural networks
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Wide residual networks
S. Zagoruyko and N. Komodakis · 2016
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Data augmentation generative adversarial networks
A. Antoniou, A. Storkey, and H. Edwards · 2017
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Designing neural network architectures using reinforcement learning
B. Baker, O. Gupta, N. Naik, and R. Raskar · 2017
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Neural optimizer search with reinforcement learning
I. Bello, B. Zoph, V. Vasudevan, and Q. V. Le · 2017
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Smash: one-shot model architecture search through hypernetworks
A. Brock, T. Lim, J. M. Ritchie, and N. Weston · 2017
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Intriguing properties of adversarial examples
E. D. Cubuk, B. Zoph, S. S. Schoenholz, and Q. V. Le · 2017
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Dataset augmentation in feature space
T. DeVries and G. W. Taylor · 2017
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S. Xie, R. Girshick, P. Dollár, Z. Tu, and K. He · 2017
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F. Yu, D. Wang, and T. Darrell · 2017
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mixup: Beyond empirical risk minimization
H. Zhang, M. Cisse, Y. N. Dauphin, and D. Lopez-Paz · 2017
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Random erasing data augmentation
Z. Zhong, L. Zheng, G. Kang, S. Li, and Y. Yang · 2017
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Data augmentation in emotion classification using generative adversarial networks
X. Zhu, Y. Liu, Z. Qin, and J. Li · 2017
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Neural architecture search with reinforcement learning
B. Zoph and Q. V. Le · 2017
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Learning transferable architectures for scalable image recognition
B. Zoph, V. Vasudevan, J. Shlens, and Q. V. Le · 2017
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Data augmentation by pairing samples for images classification
H. Inoue · 2018
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Do better imagenet models transfer better?
S. Kornblith, J. Shlens, and Q. V. Le · 2018
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Parallel architecture and hyperparameter search via successive halving and classification
M. Kumar, G. E. Dahl, V. Vasudevan, and M. Norouzi · 2018
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Hierarchical representations for efficient architecture search
H. Liu, K. Simonyan, O. Vinyals, C. Fernando, and K. Kavukcuoglu · 2018
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Exploring the limits of weakly supervised pretraining
D. Mahajan, R. Girshick, V. Ramanathan, K. He, M. Paluri, Y. Li, A. Bharambe, and L. van der Maaten · 2018
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Simple random search provides a competitive approach to reinforcement learning
H. Mania, A. Guy, and B. Recht · 2018
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Realistic evaluation of deep semi-supervised learning algorithms
A. Oliver, A. Odena, C. Raffel, E. D. Cubuk, and I. J. Goodfellow · 2018
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Efficient neural architecture search via parameter sharing
H. Pham, M. Y. Guan, B. Zoph, Q. V. Le, and J. Dean · 2018
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Regularized evolution for image classifier architecture search
E. Real, A. Aggarwal, Y. Huang, and Q. V. Le · 2018
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Do cifar-10 classifiers generalize to cifar-10?
B. Recht, R. Roelofs, L. Schmidt, and V. Shankar · 2018
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Requests For Research 2.0
I. Sutskever, J. Schulman, T. Salimans, and D. Kingma · 2018
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Y. Yamada, M. Iwamura, and K. Kise · 2018
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