插补(统计学)
计算机科学
缺少数据
鉴别器
数据挖掘
图像(数学)
人工智能
发电机(电路理论)
数据集
数据质量
生成对抗网络
图像质量
模式识别(心理学)
机器学习
工程类
功率(物理)
量子力学
探测器
公制(单位)
运营管理
电信
物理
作者
Dongwook Lee,Junyoung Kim,Won-Jin Moon,Jong Chul Ye
标识
DOI:10.1109/cvpr.2019.00259
摘要
In many applications requiring multiple inputs to obtain a desired output, if any of the input data is missing, it often introduces large amounts of bias. Although many techniques have been developed for imputing missing data, the image imputation is still difficult due to complicated nature of natural images. To address this problem, here we proposed a novel framework for missing image data imputation, called Collaborative Generative Adversarial Network (CollaGAN). CollaGAN convert the image imputation problem to a multi-domain images-to-image translation task so that a single generator and discriminator network can successfully estimate the missing data using the remaining clean data set. We demonstrate that CollaGAN produces the images with a higher visual quality compared to the existing competing approaches in various image imputation tasks.
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