论文标题
自动搜索U-NET图像转换器体系结构
Automatically Searching for U-Net Image Translator Architecture
论文作者
论文摘要
图像转换器已成功应用于许多重要的低级图像处理任务。但是，像U-NET这样的图像翻译器的经典网络体系结构是从其他视觉任务等诸如生物医学图像分割等视觉任务中借来的。这种直接的适应可能不是最佳的，并且可能导致网络结构的冗余。在本文中，我们为图像转换器提出了一种自动架构搜索方法。通过利用进化算法，我们研究了一个更有效的网络体系结构，该网络体系结构的计算资源成本较小，并且比原始的网络架构更高。进行了广泛的定性和定量实验，以证明该方法的有效性。此外，我们将搜索的网络体系结构移植到其他数据集中，而这些数据集不涉及架构搜索过程。这些数据集上搜索的体系结构的效率进一步证明了该方法的概括。
Image translators have been successfully applied to many important low level image processing tasks. However, classical network architecture of image translator like U-Net, is borrowed from other vision tasks like biomedical image segmentation. This straightforward adaptation may not be optimal and could cause redundancy in the network structure. In this paper, we propose an automatic architecture searching method for image translator. By utilizing evolutionary algorithm, we investigate a more efficient network architecture which costs less computation resources and achieves better performance than the original one. Extensive qualitative and quantitative experiments are conducted to demonstrate the effectiveness of the proposed method. Moreover, we transplant the searched network architecture to other datasets which are not involved in the architecture searching procedure. Efficiency of the searched architecture on these datasets further demonstrates the generalization of the method.
