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Datasets with missing values are very common in real world applications.GAIN, a recently proposed deep generative model for missing data imputation,has been proved to outperform many state-of-the-art methods. But GAIN only usesa reconstruction loss in the generator to minimize the imputation error of thenon-missing part, ignoring the potential category information which can reflectthe relationship between samples. In this paper, we propose a novelunsupervised missing data imputation method named PC-GAIN, which utilizespotential category information to further enhance the imputation power.Specifically, we first propose a pre-training procedure to learn potentialcategory information contained in a subset of low-missing-rate data. Then anauxiliary classifier is determined using the synthetic pseudo-labels. Further,this classifier is incorporated into the generative adversarial framework tohelp the generator to yield higher quality imputation results. The proposedmethod can improve the imputation quality of GAIN significantly. Experimentalresults on various benchmark datasets show that our method is also superior toother baseline approaches. Our code is available at\url{https://github.com/WYu-Feng/pc-gain}.

PC-GAIN: Pseudo-label Conditional Generative Adversarial Imputation Networks for Incomplete Data