When dealing with the real world data, we encounter three problems such as 1) missing data, 2) class imbalance, and 3) missing label problems. In this paper, we propose HexaGAN, a generative adversarial network framework that shows promising classification performance for all three problems.
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Reference: Uiwon Hwang, Dahuin Jung, Sungroh Yoon. “HexaGAN: Generative Adversarial Nets for Real World Classification.” International Conference on Machine Learning (ICML), 2019
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Paper URL: http://proceedings.mlr.press/v97/hwang19a/hwang19a.pdf
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Appendix URL: http://proceedings.mlr.press/v97/hwang19a/hwang19a-supp.pdf
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Presentation PPT: https://icml.cc/media/Slides/icml/2019/halla(11-14-00)-11-15-15-4629-hexagan_genera.pdf
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ops.py: various operations for building neural networks and data loading
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ops_cnn.py: various operations for convolutional neural networks (for the MNIST dataset)
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model.py: HexaGAN model (for the breast dataset)
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train_breast.py: classification on the breast dataset with 20% missingness
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train_mnist.py: missing data imputation on the MNIST dataset with 50% missingness (including HexaGAN model)