GAIN: Missing Data Imputation using Generative Adversarial Nets

TL;DR

GAIN introduces a GAN-based approach for missing data imputation, where a generator predicts missing values conditioned on observed data, and a discriminator distinguishes observed from imputed components, leading to improved imputation accuracy.

Contribution

This paper presents GAIN, a novel GAN-based framework for data imputation that effectively learns the data distribution using a hint mechanism to guide the discriminator.

Findings

GAIN outperforms existing imputation methods on various datasets

The hint mechanism improves the generator's ability to learn the true data distribution

GAIN achieves higher imputation accuracy compared to state-of-the-art techniques

Abstract

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.