计算机科学
人工智能
风格(视觉艺术)
卷积神经网络
像素
基本事实
感知
图像(数学)
转化(遗传学)
分辨率(逻辑)
计算机视觉
传输(计算)
视觉艺术
并行计算
艺术
心理学
神经科学
作者
Justin Johnson,Alexandre Alahi,Feifei Li
标识
DOI:10.1007/978-3-319-46475-6_43
摘要
We consider image transformation problems, where an input image is transformed into an output image. Recent methods for such problems typically train feed-forward convolutional neural networks using a per-pixel loss between the output and ground-truth images. Parallel work has shown that high-quality images can be generated by defining and optimizing perceptual loss functions based on high-level features extracted from pretrained networks. We combine the benefits of both approaches, and propose the use of perceptual loss functions for training feed-forward networks for image transformation tasks. We show results on image style transfer, where a feed-forward network is trained to solve the optimization problem proposed by Gatys et al. in real-time. Compared to the optimization-based method, our network gives similar qualitative results but is three orders of magnitude faster. We also experiment with single-image super-resolution, where replacing a per-pixel loss with a perceptual loss gives visually pleasing results.
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