2021年8月10日 · This work introduces a novel iterative GAN architecture, called Iterative Generative Adversarial Networks for Imputation (IGANI), for data imputation. IGANI imputes data in two steps and maintains the invertibility of the generative imputer, which will be shown to be a sufficient condition for the convergence of the proposed GAN-based imputation.

are implicit generative models that can be used for data imputation as an unsupervised learning problem. This works introduce. an iterative GAN architecture for data imputation based on the invertibility of the generati.

The architecture, called Iterative Generative Adversarial Networks for Imputation (IGANI), is simple as it enjoys one generator and discriminator. The generator is iterated over imputed data and the intuition behind such iteration is to train a robust discriminator which can identify the first-hand imputed data as real and the second-hand one ...

2021年8月10日 · This work introduces a novel iterative GAN architecture, called Iterative Generative Adversarial Networks for Imputation (IGANI), for data imputation.

2020年8月11日 · This work introduces a novel iterative GAN architecture, called Iterative Generative Adversarial Networks for Imputation (IGANI), for data imputation. IGANI imputes data in two steps and maintains the invertibility of the generative imputer, which will be shown to be a sufficient condition for the convergence of the proposed GAN-based imputation.

2020年8月11日 · Generative adversarial networks (GANs) are implicit generative models that can be used for data imputation as an unsupervised learning problem. This works...

IGANI: Iterative Generative Adversarial Networks for Imputation With Application to Traffic Data Kazemi, Amir; Meidani, Hadi; Abstract. Publication: IEEE Access. Pub Date: 2021 DOI: 10.1109/ACCESS.2021.3103456 Bibcode: 2021IEEEA...9k2966K full text sources ...

2020年8月11日 · This work introduces a novel iterative GAN architecture, called Iterative Generative Adversarial Networks for Imputation (IGANI), for data imputation, and shows that the proposed algorithm mostly produces more accurate results compared to those of previous GAN-based imputation architectures.

2020年8月11日 · Generative adversarial networks (GANs) are implicit generative models that can be used for data imputation as an unsupervised learning problem. This works introduces an iterative GAN architecture for data imputation based on the invertibility of the generative imputer.

igani has one repository available. Follow their code on GitHub.