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Generative Adversarial Network for missing data imputation

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Advanced_GAIN

An attempt to boost the performance of PyTorch implementation of(https://arxiv.org/abs/1806.02920).

  • The code is taken from (https://github.com/dhanajitb/GAIN-Pytorch). The datasets are also present in the amazing repository. The code of generator and discriminator is modified in the notebook. However results are not so good.

  • The idea is to use Network Deconvolution(https://arxiv.org/abs/1905.11926) model in generator and discriminator to improve the performance.

  • Network deconvolution code: (https://arxiv.org/abs/1905.11926)

  • The notebook is tested on Python 3.6 and PyTorch 1.4.0.

  • The code can be run in either GPU or CPU (using use_gpu flag).

Contributing

  • For major changes, please open an issue first to discuss what you would like to change.

  • If you want to contribute please:

  1. Fork the Project
  2. Create your Feature Branch (git checkout -b <your_branch_name>)
  3. Stage your Changes (git add .)
  4. Commit your Changes (git commit -m '<your_commit_message>')
  5. Push to the Branch (git push origin <your_branch_name>)
  6. Open a Pull Request

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