缩放绘画风格迁移
Scaling Painting Style Transfer
AI总结：
本文提出一种通过空间局部化VGG网络计算的方法，实现超高分辨率绘画风格迁移，在保持质量的同时，解决了传统NST的计算昂贵和分辨率限制问题。
摘要：
神经风格迁移（NST）是一种深度学习技术，能够将风格图像的风格迁移到内容图像上，产生前所未有的丰富风格效果。在将绘画风格迁移到图像时，该技术尤为引人注目。最初的NST通过求解优化问题来实现，即在保留内容图像局部几何特征的同时，匹配风格图像的全局统计特征。这种方法的主要缺点有两个：计算成本高，以及输出图像的分辨率受限于高GPU内存需求。许多解决方案已被提出，旨在加速NST并生成更大尺寸的图像。然而，我们的调查表明，这些加速方法在绘画风格迁移的背景下，都损害了生成图像的质量。实际上，迁移绘画风格是一项复杂任务，涉及不同尺度的特征，从调色板和构图风格到精细的笔触和画布纹理。本文提供了一种解决方案，用于解决超高分辨率（UHR）图像的原始全局优化问题，使得多尺度NST能够以前所未有的图像尺寸进行。这是通过空间局部化VGG网络中每次前向和反向传播的计算来实现的。广泛的定性比较和定量比较，以及一项感知研究，表明我们的方法在如此高分辨率的绘画风格迁移中，产生了无与伦比的质量。通过仔细比较，我们表明最先进的快速方法仍然容易产生伪影，从而表明快速绘画风格迁移仍然是一个开放问题。源代码可在 https://github.com/bgalerne/scaling_painting_style_transfer 获取。
英文摘要：
Neural style transfer (NST) is a deep learning technique that produces an unprecedentedly rich style transfer from a style image to a content image. It is particularly impressive when it comes to transferring style from a painting to an image. NST was originally achieved by solving an optimization problem to match the global statistics of the style image while preserving the local geometric features of the content image. The two main drawbacks of this original approach is that it is computationally expensive and that the resolution of the output images is limited by high GPU memory requirements. Many solutions have been proposed to both accelerate NST and produce images with larger size. However, our investigation shows that these accelerated methods all compromise the quality of the produced images in the context of painting style transfer. Indeed, transferring the style of a painting is a complex task involving features at different scales, from the color palette and compositional style to the fine brushstrokes and texture of the canvas. This paper provides a solution to solve the original global optimization for ultra-high resolution (UHR) images, enabling multiscale NST at unprecedented image sizes. This is achieved by spatially localizing the computation of each forward and backward passes through the VGG network. Extensive qualitative and quantitative comparisons, as well as a \textcolor{coverletter}{perceptual study}, show that our method produces style transfer of unmatched quality for such high-resolution painting styles. By a careful comparison, we show that state-of-the-art fast methods are still prone to artifacts, thus suggesting that fast painting style transfer remains an open problem. Source code is available at https://github.com/bgalerne/scaling_painting_style_transfer.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
