论文标题
半监督图像使用高斯过程
Semi-Supervised 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。更具体地说，我们使用高斯进程对未标记数据的潜在空间向量进行建模，然后将其用于计算伪沿图，以监督网络未标记的数据。通过对几个具有挑战性的数据集（例如Rain800，Rain200L和DDN-SIRR）进行的大量实验和消融，我们表明该提出的方法能够有效利用未标记的数据，从而与仅标签培训相比，可以提高性能。此外，我们证明了在拟议的基于GP的框架结果中使用未标记的现实世界图像
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. More specifically, we model the latent space vectors of unlabeled data using Gaussian Processes, which is then used to compute pseudo-ground-truth for supervising the network on unlabeled data. Through extensive experiments and ablations on several challenging datasets (such as Rain800, Rain200L and DDN-SIRR), we show that the proposed method is able to effectively leverage unlabeled data thereby resulting in significantly better performance as compared to labeled-only training. Additionally, we demonstrate that using unlabeled real-world images in the proposed GP-based framework results
