A CWGAN model with regression.
we proposed a novel virtual sample generation method embedding a deep neural network as a regressor into conditional Wasserstein generative adversarial networks with gradient penalty (rCWGAN). In rCWGAN, conditional variables are introduced making the training supervised and a dual training algorithm is specially designed. With the advanced structure and training algorithm, the model has powerful sample generation capabilities and can handle prediction problems of quality variable.


Python 3.6


  • numpy
  • pandas
  • matplotlib
  • tensorflow
  • keras

all packages need to be installed on a conda environment with python >= 3.0


We appreciate efforts in https://github.com/eriklindernoren/Keras-GAN and in https://github.com/mkirchmeyer/ganRegression.


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