摘要
由一名新闻记者兼机器人与机器学习每日新闻的工作人员新闻编辑-调查人员发布了关于公司智能的新报告。根据NewsRx编辑对中国人民共和国陕西外的新闻报道，研究表明："以改进的Johnso N等人的风格迁移网络为出发点，提出了一种基于位置信息图矩阵的新w损失函数"。我们的新闻记者从网络信息中心的研究中获得了一句话：“新方法将位置信息添加到分块的Gram矩阵中，同时将风格图与原始图像的结构相似性添加到风格训练中，将风格位置信息添加到合成图像中，最后，”仿真结果表明,新模型的图像处理结果优于经典Johnson等人的Met Hod、Google Brain Team Method等人的Met Hod、Google Brain Team Method等人的Met Hod、Google Brain Team等人的结果地图和风格图像的SIM值均大于0.3.作为比较，Johnson等人、Google Brain Team和CCPL的SIM值分别为0.14、0.11和0.12，有明显的改善。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on co mputational intelligence. According to news reporting out of Shaanxi, People’s R epublic of China, by NewsRx editors, research stated, “Using the improved Johnso n et al.’s style migration network as a starting point, this paper proposes a ne w loss function based on the position information Gram matrix.” Our news journalists obtained a quote from the research from Network Information Center: “The new method adds the chunked Gram matrix with position information, and simultaneously, the structural similarity between the style map and the res ultant image is added to the style training. The style position information is g iven to the resultant image, and finally, the resolution of the resultant image is improved with the SRGAN. The new model can effectively migrate the texture st ructure as well as the color space of the style image, while the data of the con tent image are kept intact. The simulation results reveal that the image process ing results of the new model improve those of the classical Johnson et al.’s met hod, Google Brain team method, and CCPL method, and the SSIM values of the resul ting map and style image are all greater than 0.3. As a comparison, the SSIM val ues of Johnson et al., Google Brain team, and CCPL are 0.14, 0.11, and 0.12, res pectively, which is an obvious improvement.”
NETL
NSTL