论文标题
通过最佳运输来实现地面真理
Ground Truth Free Denoising by Optimal Transport
论文作者
论文摘要
我们为任意类型的数据提供了一种学习的无监督的denoising方法，我们在图像和一维信号上探讨了该方法。该培训仅基于嘈杂的数据和噪声示例的样本，这些样本 - 至关重要的是 - 不需要成对。我们只需要假设噪声是独立和加性的（尽管我们描述了如何扩展）。该方法基于Wasserstein生成的对抗网络设置，该设置利用了两个评论家和一个发电机。
We present a learned unsupervised denoising method for arbitrary types of data, which we explore on images and one-dimensional signals. The training is solely based on samples of noisy data and examples of noise, which -- critically -- do not need to come in pairs. We only need the assumption that the noise is independent and additive (although we describe how this can be extended). The method rests on a Wasserstein Generative Adversarial Network setting, which utilizes two critics and one generator.
