Variational cycle-consistent imputation adversarial networks for general missing patterns

Imputation of missing data is an important but challenging issue because we do not know the underlying distribution of the missing data. Previous imputation models have addressed this problem by assuming specific kinds of missing distributions. However, in practice, the mechanism of the missing data is unknown, so the most general case of missing pattern needs to be considered for successful imputation. In this paper, we present cycle-consistent imputation adversarial networks to discover the underlying distribution of missing patterns closely under some relaxations. Using adversarial training, our model successfully learns the most general case of missing patterns. Therefore our method can be applied to a wide variety of imputation problems. We empirically evaluated the proposed method with numerical and image data. The result shows that our method yields the state-of-the-art performance quantitatively and qualitatively on standard datasets.