Fetching the paper…
Reading the bibliography…
Data augmentation is a ubiquitous technique for increasing the size of labeled training sets by leveraging task-specific data transformations that preserve class labels.
Gradient-based learning applied to document recognition
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner · 1998
Earlier work this paper cites.
The digital database for screening mammography
M. Heath, K. Bowyer, D. Kopans, R. Moore, and W. P. Kegelmeyer · 2000
Earlier work this paper cites.
Smote: synthetic minority over-sampling technique
N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer · 2002
Earlier work this paper cites.
Poisson image editing
P. Pérez, M. Gangnet, and A. Blake · 2003
Earlier work this paper cites.
The automatic content extraction (ace) program-tasks, data, and evaluation
G. R. Doddington, A. Mitchell, M. A. Przybocki, L. A. Ramshaw, S. Strassel, and R. M. Weischedel · 2004
Earlier work this paper cites.
Variance reduction techniques for gradient estimates in reinforcement learning
E. Greensmith, P. L. Bartlett, and J. Baxter · 2004
Earlier work this paper cites.
Rcv1: A new benchmark collection for text categorization research
D. D. Lewis, Y. Yang, T. G. Rose, and F. Li · 2004
Earlier work this paper cites.
Enhancing text categorization with semantic-enriched representation and training data augmentation
X. Lu, B. Zheng, A. Velivelli, and C. Zhai · 2006
Earlier work this paper cites.
Convex learning with invariances
C. H. Teo, A. Globerson, S. T. Roweis, and A. J. Smola · 2008
Earlier work this paper cites.
Learning multiple layers of features from tiny images
A. Krizhevsky and G. Hinton · 2009
Earlier work this paper cites.
Deep big simple neural nets excel on handwritten digit recognition, 2010
D. C. Ciresan, U. Meier, L. M. Gambardella, and J. Schmidhuber · 2010
Earlier work this paper cites.
D. Wierstra, A. Förster, J. Peters, and J. Schmidhuber · 2010
Earlier work this paper cites.
The cancer imaging archive (TCIA): Maintaining and operating a public information repository
K. Clark, B. Vendt, K. Smith, J. Freymann, J. Kirby, P. Koppel, S. Moore, S. Phillips, D. Maffitt, M. Pringle, L. Tarbox, and F. Prior · 2013
Cited alongside, same era.
Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
Cited alongside, same era.
Explaining and harnessing adversarial examples
I. J. Goodfellow, J. Shlens, and C. Szegedy · 2014
Cited alongside, same era.
B. Graham · 2014
Cited alongside, same era.
Conditional generative adversarial nets
M. Mirza and S. Osindero · 2014
Cited alongside, same era.
Adaptive data augmentation for image classification
A. Fawzi, H. Samulowitz, D. Turaga, and P. Frossard · 2016
Later among the works it cites.
Dreaming more data: Class-dependent distributions over diffeomorphisms for learned data augmentation
S. Hauberg, O. Freifeld, A. B. L. Larsen, J. Fisher, and L. Hansen · 2016
Later among the works it cites.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Later among the works it cites.
Densely connected convolutional networks
G. Huang, Z. Liu, K. Q. Weinberger, and L. van der Maaten · 2016
Later among the works it cites.
Regularization with stochastic transformations and perturbations for deep semi-supervised learning
M. Sajjadi, M. Javanmardi, and T. Tasdizen · 2016
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
A. Dosovitskiy, P. Fischer, J. Springenberg, M. Riedmiller, and T. Brox · 2015
Cited alongside, same era.
Distributional smoothing with virtual adversarial training
T. Miyato, S.-i. Maeda, M. Koyama, K. Nakae, and S. Ishii · 2015
Cited alongside, same era.
Unsupervised representation learning with deep convolutional generative adversarial networks
A. Radford, L. Metz, and S. Chintala · 2015
Cited alongside, same era.
Gradient estimation using stochastic computation graphs
J. Schulman, N. Heess, T. Weber, and P. Abbeel · 2015
Cited alongside, same era.
Unsupervised and semi-supervised learning with categorical generative adversarial networks
J. T. Springenberg · 2015
Cited alongside, same era.
Enriching word vectors with subword information
P. Bojanowski, E. Grave, A. Joulin, and T. Mikolov · 2016
Cited alongside, same era.
Improved techniques for training gans
T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen · 2016
Later among the works it cites.
Curated breast imaging subset of DDSM
R. Sawyer Lee, F. Gimenez, A. Hoogi, and D. Rubin · 2016
Later among the works it cites.
Rendergan: Generating realistic labeled data
L. Sixt, B. Wild, and T. Landgraf · 2016
Later among the works it cites.
Adversarial transformation networks: Learning to generate adversarial examples
S. Baluja and I. Fischer · 2017
Closest in time.
Dataset augmentation in feature space
T. DeVries and G. W. Taylor · 2017
Closest in time.
Improving music source separation based on deep neural networks through data augmentation and network blending
S. Uhlich, M. Porcu, F. Giron, M. Enenkl, T. Kemp, N. Takahashi, and Y. Mitsufuji · 2017
Closest in time.