ClueGAIN: Application of Transfer Learning On Generative Adversarial Imputation Nets (GAIN)
This addresses data imputation challenges for researchers dealing with incomplete datasets, but it is incremental as it builds on existing GAIN methods.
The paper tackles poor imputation performance in high missing rate datasets by introducing transfer learning into Generative Adversarial Imputation Nets (GAIN), resulting in improved performance as indicated by good results.
Many studies have attempted to solve the problem of missing data using various approaches. Among them, Generative Adversarial Imputation Nets (GAIN) was first used to impute data with Generative Adversarial Nets (GAN) and good results were obtained. Subsequent studies have attempted to combine various approaches to address some of its limitations. ClueGAIN is first proposed in this study, which introduces transfer learning into GAIN to solve the problem of poor imputation performance in high missing rate data sets. ClueGAIN can also be used to measure the similarity between data sets to explore their potential connections.