[1603.08155] Perceptual Losses for Real-Time Style Transfer and Super-Resolution

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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 \emph{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 \emph{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.

arXiv:1603.08155v1 [cs.CV] 27 Mar 2016 Perceptual Losses for Real-Time Style Transfer and Super-Resolution Justin Johnson, Alexandre Alahi, Li Fei-Fei {jcjohns, alahi, feifeili}@cs.stanford.edu Department of Computer Science, Stanford University Abstract. We consider image transformation…

[1603.08155] Perceptual Losses for Real-Time Style Transfer and Super-Resolution

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