Missing data represent a main issue having a considerable impact not only on the inference quality but also on the prediction relevance. A problem that researchers have constantly tried to solve, ranging from Statistics to Deep Learning through Machine Learning. Recently, a growing interest is being given to Deep Learning models and particularly to Generative Adversarial Networks (GAN) given their interesting performance and their adaptability to various scenarios. In this paper, we propose an improvement of Generative Adversarial Imputation Nets (GAIN) which is one of the main GAN-based models having proved its worth in image imputation. The results found confirm the proposed improvement efficiency.
Missing data represent a main issue having a considerable impact not only on the inference quality but also on the prediction relevance. A problem that researchers have constantly tried to solve, ranging from Statistics to Deep Learning through Machine Learning. Recently, a growing interest is being given to Deep Learning models and particularly to Generative Adversarial Networks (GAN) given their interesting performance and their adaptability to various scenarios. In this paper, we propose an improvement of Generative Adversarial Imputation Nets (GAIN) which is one of the main GAN-based models having proved its worth in image imputation. The results found confirm the proposed improvement efficiency.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.