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 converts an 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.
翻译:在许多应用程序中,如果缺少任何输入数据,需要多种投入才能获得理想的输出,它往往会引入大量偏差。虽然为估算缺失数据开发了许多技术,但由于自然图像的复杂性质,图像估算仍然困难。为了解决这一问题,我们在这里提出了一个丢失图像数据估算的新框架,称为“协作生成反versarial网络(CollaGAN) ”。 CollaGAN将图像估算问题转换为多域图像到图像翻译任务,以便一个单一的生成器和导师网络能够利用其余的干净数据集成功估算缺失数据。我们证明,与各种图像估算任务中现有的相互竞争的方法相比,CollaGAN生成图像的视觉质量更高。