swMATH ID: 33872
Software Authors: Jinsung Yoon, James Jordon, Mihaela van der Schaar
Description: GAIN: Missing Data Imputation using Generative Adversarial Nets. We propose a novel method for imputing missing data by adapting the well-known Generative Adversarial Nets (GAN) framework. Accordingly, we call our method Generative Adversarial Imputation Nets (GAIN). The generator (G) observes some components of a real data vector, imputes the missing components conditioned on what is actually observed, and outputs a completed vector. The discriminator (D) then takes a completed vector and attempts to determine which components were actually observed and which were imputed. To ensure that D forces G to learn the desired distribution, we provide D with some additional information in the form of a hint vector. The hint reveals to D partial information about the missingness of the original sample, which is used by D to focus its attention on the imputation quality of particular components. This hint ensures that G does in fact learn to generate according to the true data distribution. We tested our method on various datasets and found that GAIN significantly outperforms state-of-the-art imputation methods.
Homepage: https://arxiv.org/abs/1806.02920
Related Software: missForest; MICE; MisGAN; Wasserstein GAN; Adam; CollaGAN; LambdaMART; LightGBM; BRITS; MaskGAN; SeqGAN; AmbientGAN; pix2pix; CycleGAN; Auto-WEKA; XGBoost; Spearmint; randomForest; KnockoffGAN; GitHub
Cited in: 8 Documents

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