Generation of Synthetic Data with Generative Adversarial Networks

The aim of synthetic data generation is to provide data that is not real for cases where the use of real data is somehow limited. For example, when there is a need for larger volumes of data, when ...

[1]  Stefan Leijnen,et al.  The Neural Network Zoo , 2020 .

[2]  H Surendra,et al.  A Review Of Synthetic Data Generation Methods For Privacy Preserving Data Publishing , 2017 .

[3]  Trevor Darrell,et al.  Adversarial Feature Learning , 2016, ICLR.

[4]  Jeffrey L. Elman,et al.  Finding Structure in Time , 1990, Cogn. Sci..

[5]  Gunnar Rätsch,et al.  Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs , 2017, ArXiv.

[6]  Rui Xiao,et al.  Development of a Synthetic Data Set Generator for Building and Testing Information Discovery Systems , 2006, Third International Conference on Information Technology: New Generations (ITNG'06).

[7]  Marc G. Bellemare,et al.  The Cramer Distance as a Solution to Biased Wasserstein Gradients , 2017, ArXiv.

[8]  Yoshua Bengio,et al.  Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling , 2014, ArXiv.

[9]  Anne Håkansson,et al.  Portal of Research Methods and Methodologies for Research Projects and Degree Projects , 2013 .

[10]  Léon Bottou,et al.  Wasserstein GAN , 2017, ArXiv.

[11]  Hui Li,et al.  An Overview of Deep Generative Models , 2015 .

[12]  Yoshua Bengio,et al.  Generative Adversarial Networks , 2014, ArXiv.

[13]  Arthur L. Samuel,et al.  Some Studies in Machine Learning Using the Game of Checkers , 1967, IBM J. Res. Dev..

[14]  Michael I. Jordan,et al.  On Discriminative vs. Generative Classifiers: A comparison of logistic regression and naive Bayes , 2001, NIPS.

[15]  Taoxin Peng,et al.  Towards a Synthetic Data Generator for Matching Decision Trees , 2016, ICEIS.

[16]  Rick Wicklin.  Simulating Data with SAS , 2013 .

[17]  Aaron C. Courville,et al.  Improved Training of Wasserstein GANs , 2017, NIPS.

[18]  Isabelle Guyon,et al.  Design and analysis of the KDD cup 2009: fast scoring on a large orange customer database , 2009, SKDD.

[19]  Jimeng Sun,et al.  Generating Multi-label Discrete Patient Records using Generative Adversarial Networks , 2017, MLHC.

[20]  Craig W. Thompson,et al.  Generating Synthetic Data to Match Data Mining Patterns , 2008, IEEE Internet Computing.

[21]  Pieter Abbeel,et al.  InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets , 2016, NIPS.

[22]  Simon Osindero,et al.  Conditional Generative Adversarial Nets , 2014, ArXiv.

[23]  Max Welling,et al.  Auto-Encoding Variational Bayes , 2013, ICLR.