arXivDaily arXiv每日学术速递 周一至周五更新
arXiv周末暂无论文更新,休息一下吧,周末愉快~~
arXiv 2012.02992cs.CV
收藏

用于快速图像平移的空间自适应像素网络

Spatially-Adaptive Pixelwise Networks for Fast Image Translation

Technion(以色列理工学院);Adobe Research(奥多比研究院)

Tamar Rott Shaham, Michael Gharbi, Richard Zhang, Eli Shechtman, Tomer Michaeli

摘要:

我们引入了一个新的生成器架构,旨在快速,高效的高分辨率图像到图像的翻译。我们将生成器设计为全分辨率图像的一个非常轻量级的函数。事实上,我们使用的是逐像素网络;也就是说,每个像素都是通过简单的仿射变换和非线性的组合来独立处理的。我们采取了三个重要步骤来为这样一个看似简单的函数提供足够的表达能力。首先,逐像素网络的参数在空间上是变化的,因此它们可以代表比简单的1x 1卷积更广泛的函数类。其次,这些参数是由一个快速卷积网络预测的,该网络处理输入的低分辨率表示;第三,我们用空间坐标的正弦编码来增强输入图像,这为生成逼真的新颖高频图像内容提供了有效的归纳偏差。因此,我们的模型比最先进的基线快18倍。我们实现了这种加速,同时在不同的图像分辨率和翻译域中生成可比的视觉质量。

英文摘要:

We introduce a new generator architecture, aimed at fast and efficient high-resolution image-to-image translation. We design the generator to be an extremely lightweight function of the full-resolution image. In fact, we use pixel-wise networks; that is, each pixel is processed independently of others, through a composition of simple affine transformations and nonlinearities. We take three important steps to equip such a seemingly simple function with adequate expressivity. First, the parameters of the pixel-wise networks are spatially varying so they can represent a broader function class than simple 1x1 convolutions. Second, these parameters are predicted by a fast convolutional network that processes an aggressively low-resolution representation of the input; Third, we augment the input image with a sinusoidal encoding of spatial coordinates, which provides an effective inductive bias for generating realistic novel high-frequency image content. As a result, our model is up to 18x faster than state-of-the-art baselines. We achieve this speedup while generating comparable visual quality across different image resolutions and translation domains.

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。

↑