Title: GAIN: Missing Data Imputation using Generative Adversarial Nets
Authors: Jinsung Yoon, James Jordon, Mihaela van der Schaar
Reference: J. Yoon, J. Jordon, M. van der Schaar, "GAIN: Missing Data Imputation using Generative Adversarial Nets," International Conference on Machine Learning (ICML), 2018.
Paper Link: http://medianetlab.ee.ucla.edu/papers/ICML_GAIN.pdf
Appendix Link: http://medianetlab.ee.ucla.edu/papers/ICML_GAIN_Supp.pdf
Description of the code
This code shows the implementation of GAIN on MNIST dataset.
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Introducing 50% of missingness on MNIST dataset.
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Recover missing values on MNIST datasets using GAIN.
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Show the multiple imputation results on MNIST with GAIN.
Add source codes for UCI Letter and Spam datasets (02/12/2019)