论文标题
Syn2Real传输学习用于使用高斯过程的图像驱动
Syn2Real Transfer Learning for Image Deraining using Gaussian Processes
论文作者
论文摘要
在重建误差以及视觉质量方面，基于CNN的最新基于CNN的方法已实现了出色的性能。但是，这些方法只能在完全标记的数据上接受培训，从而受到限制。由于在获取现实世界全标记的数据集方面面临的各种挑战，现有方法仅在合成生成的数据上进行培训，因此，对现实世界的图像的推广不佳。在文献中，在训练图像中使用现实数据的使用相对较少。我们提出了一个基于高斯流程的半监督学习框架，该框架使网络可以使用合成数据集进行学习，同时使用未标记的现实世界图像更好地概括。通过对几个具有挑战性的数据集（例如Rain 800，Rain 200H和DDN-SIRR）进行大量实验和消融，我们表明，在使用有限标记的数据进行培训时，提出的方法通过全标签的培训实现了PAR性能。此外，我们证明，与现有方法相比，在提出的基于GP的框架中使用未标记的现实世界图像可产生卓越的性能。代码可在以下网址找到：https：//github.com/rajeevyasarla/syn2real
Recent CNN-based methods for image deraining have achieved excellent performance in terms of reconstruction error as well as visual quality. However, these methods are limited in the sense that they can be trained only on fully labeled data. Due to various challenges in obtaining real world fully-labeled image deraining datasets, existing methods are trained only on synthetically generated data and hence, generalize poorly to real-world images. The use of real-world data in training image deraining networks is relatively less explored in the literature. We propose a Gaussian Process-based semi-supervised learning framework which enables the network in learning to derain using synthetic dataset while generalizing better using unlabeled real-world images. Through extensive experiments and ablations on several challenging datasets (such as Rain800, Rain200H and DDN-SIRR), we show that the proposed method, when trained on limited labeled data, achieves on-par performance with fully-labeled training. Additionally, we demonstrate that using unlabeled real-world images in the proposed GP-based framework results in superior performance as compared to existing methods. Code is available at: https://github.com/rajeevyasarla/Syn2Real
