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In this paper we propose the use of Generative Adversarial Networks (GAN) to generate artificial training data for machine learning tasks.
Unsupervised and semi-supervised learning with categorical generative adversarial networks
Jost Tobias Springenberg · 1904
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Using the adap learning algorithm to forecast the onset of diabetes mellitus
J.W. Smith, J.E. Everhart, W.C. Dickson, W.C.Knowler, and R.S. Johannes · 1988
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Adasyn: Adaptive synthetic sampling approach for imbalanced learning
Haibo He, Yang Bai, Edwardo A. Garcia, and Shutao Li · 2008
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Smote: Synthetic minority over-sampling technique
N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer · 2011
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A novel boundary oversampling algorithm based on neighborhood rough set model: Nrsboundary-smote
Feng Hu and Hang Li · 2013
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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How to train a gan? tips and tricks to make gans work, 2016
Soumith Chintala, Emily Denton, Martin Arjovsky, and Michael Mathieu · 2016
Cited alongside, same era.
Credit card fraud detection: A realistic modeling and a novel learning strategy
Andrea Dal Pozzolo, Giacomo Boracchi, Olivier Caelen, Cesare Alippi, and Gianluca Bontempi · 2017
Cited alongside, same era.
UCI machine learning repository, 2017
Dheeru Dua and Casey Graff · 2017
Cited alongside, same era.
The cornucopia of meaningful leads: Applying deep adversarial autoencoders for new molecule development in oncology
Artur Kadurin, Alexander Aliper, Andrey Kazennov, Polina Mamoshina, Quentin Vanhaelen, Kuzma Khrabrov, and Alex Zhavoronkov · 2017
Cited alongside, same era.
What are some recent and potentially upcoming breakthroughs in deep learning?, July 2017
Yann LeCun · 2017
Cited alongside, same era.
Photo-realistic single image super-resolution using a generative adversarial network
Christian Ledig, Lucas Theis, Ferenc Huszár, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, et al · 2017
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Imbalanced-learn: A python toolbox to tackle the curse of imbalanced datasets in machine learning
Guillaume Lemaître, Fernando Nogueira, and Christos K. Aridas · 2017
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Synthetic data augmentation using gan for improved liver lesion classification
Maayan Frid-Adar, Eyal Klang, Michal Amitai, Jacob Goldberger, and Hayit Greenspan · 2018
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Progressive growing of GANs for improved quality, stability, and variation
Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen · 2018
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Bagan: Data augmentation with balancing gan, 2018
Giovanni Mariani, Florian Scheidegger, Roxana Istrate, Costas Bekas, and Cristiano Malossi · 2018
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