Missing View Imputation with Generative Adversarial Networks

Domain mappings and view correspondences

This package implements an approach for missing view and missing data imputation via generative adversarial networks (GANs), which we name as VIGAN. It combines cross-domain relations given unpaired data with multi-view relations given paired data. This approach first treats each view as a separate domain and identifies domain-to-domain mappings through... [Read More]

Generative Adversarial Networks

Papers and Codes.

Generative Adversarial Networks (GANs) is a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather... [Read More]