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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.
Author Information
Jinsung Yoon (University of California, Los Angeles)
James Jordon (University of Oxford)
Mihaela van der Schaar (University of Oxford)
Related Events (a corresponding poster, oral, or spotlight)
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2018 Oral: GAIN: Missing Data Imputation using Generative Adversarial Nets »
Thu. Jul 12th 01:20 -- 01:30 PM Room A7
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