GAIN is a novel method for imputing missing data using Generative Adversarial Nets (GANs). The method, called Generative Adversarial Imputation Nets (GAIN), involves a generator (G) that imputes missing data based on observed components and a discriminator (D) that distinguishes between observed and imputed components. To ensure the generator learns the true data distribution, the discriminator is provided with a hint vector that reveals partial information about the missingness of the original data. This allows the discriminator to focus on the quality of imputation for specific components. GAIN was tested on various datasets and significantly outperformed state-of-the-art imputation methods. The paper discusses the problem of missing data, which can be categorized into three types: Missing Completely at Random (MCAR), Missing at Random (MAR), and Missing Not at Random (MNAR). GAIN is evaluated under the MCAR assumption and compared with other methods. It is categorized as a generative method, which includes algorithms based on Expectation Maximization and deep learning, such as denoising autoencoders (DAEs) and GANs. However, existing generative methods have limitations, such as requiring complete data for training. GAIN is designed to work even when complete data is unavailable. The generator aims to accurately impute missing data, while the discriminator aims to distinguish between observed and imputed components. The discriminator is trained to minimize classification loss, and the generator is trained to maximize the discriminator's misclassification rate. This adversarial process is used to train the networks. The hint mechanism ensures that the generator learns the true data distribution by providing additional information to the discriminator. The paper presents a theoretical analysis of GAIN, showing that the hint mechanism is crucial for the generator to learn the desired distribution. Theoretical results demonstrate that the hint ensures the generator learns the true data distribution. The algorithm is evaluated on various real-world datasets, showing that GAIN significantly outperforms state-of-the-art imputation methods in terms of imputation accuracy and prediction performance. The method is also robust to different missing rates, numbers of samples, and feature dimensions. The results show that GAIN provides better imputation quality, leading to improved prediction accuracy. The paper concludes that GAIN is a promising method for missing data imputation and has potential applications in various domains.GAIN is a novel method for imputing missing data using Generative Adversarial Nets (GANs). The method, called Generative Adversarial Imputation Nets (GAIN), involves a generator (G) that imputes missing data based on observed components and a discriminator (D) that distinguishes between observed and imputed components. To ensure the generator learns the true data distribution, the discriminator is provided with a hint vector that reveals partial information about the missingness of the original data. This allows the discriminator to focus on the quality of imputation for specific components. GAIN was tested on various datasets and significantly outperformed state-of-the-art imputation methods. The paper discusses the problem of missing data, which can be categorized into three types: Missing Completely at Random (MCAR), Missing at Random (MAR), and Missing Not at Random (MNAR). GAIN is evaluated under the MCAR assumption and compared with other methods. It is categorized as a generative method, which includes algorithms based on Expectation Maximization and deep learning, such as denoising autoencoders (DAEs) and GANs. However, existing generative methods have limitations, such as requiring complete data for training. GAIN is designed to work even when complete data is unavailable. The generator aims to accurately impute missing data, while the discriminator aims to distinguish between observed and imputed components. The discriminator is trained to minimize classification loss, and the generator is trained to maximize the discriminator's misclassification rate. This adversarial process is used to train the networks. The hint mechanism ensures that the generator learns the true data distribution by providing additional information to the discriminator. The paper presents a theoretical analysis of GAIN, showing that the hint mechanism is crucial for the generator to learn the desired distribution. Theoretical results demonstrate that the hint ensures the generator learns the true data distribution. The algorithm is evaluated on various real-world datasets, showing that GAIN significantly outperforms state-of-the-art imputation methods in terms of imputation accuracy and prediction performance. The method is also robust to different missing rates, numbers of samples, and feature dimensions. The results show that GAIN provides better imputation quality, leading to improved prediction accuracy. The paper concludes that GAIN is a promising method for missing data imputation and has potential applications in various domains.