Perceptual Losses for Real-Time Style Transfer and Super-Resolution This project is a PyTorch implementation of Perceptual Losses for Real-Time Style Transfer and Super-Resolution. Manually re-drawing an image in a certain artistic style takes a professional artist a long time.

Instance normalization: The missing ingredient for fast stylization. The loss network is used to get content and style representations from the content and style images: (i) The content representation are taken from the layer `relu3_3`. Perceptual Losses for Real-Time Style Transfer and Super-Resolution. Springer, 2016. During training, perceptual losses measure image similarities more robustly than per-pixel losses, and at test-time the transformation networks run in real-time. We combine the benefits of both approaches, and propose the use of perceptual loss functions for training feed-forward networks for image transformation tasks. 694-711 Google Scholar
Springer…

3.D. Perceptual Losses for Real-Time Style Transfer and Super-Resolution: Supplementary Material Justin Johnson, Alexandre Alahi, Li Fei-Fei fjcjohns, alahi, feifeilig@cs.stanford.edu Department of Computer Science, Stanford University 1 Network Architectures Our style transfer networks use the architecture shown in Table 1 and our super- J. Johnson, A. Alahi, L. Fei-FeiPerceptual losses for real-time style transfer and super-resolution Proceedings of the European conference on computer vision, Springer (2016), pp. 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. Perceptual losses for real-time style transfer and super-resolution. Loss Network. Johnson, J., Alahi, A., Fei, L.F.: Perceptual losses for real-time style transfer and super-resolution. URL [3] Johnson, Justin, Alexandre Alahi, and Li Fei-Fei.

Bibliographic details on Perceptual Losses for Real-Time Style Transfer and Super-Resolution. This paper focuses on feature losses (called perceptual loss in the paper). Perceptual losses for real-time style transfer and super-resolution.

We consider image transformation problems, where an input image is transformed into an output image. We combine the benefits of both approaches, and propose the use of perceptual loss functions for training feed-forward networks for image transformation tasks. 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.
Perceptual Losses for Real-Time Style Transfer and Super-Resolution 3 in Table 1 with an equivalent non-residual block consisting of a pair of 3 3 convolutional layers with the same number of lters as shown in Figure 1. We experiment on two tasks: style transfer and single-image super-resolution. 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. "Perceptual Losses for Real-Time Style Transfer and Super-Resolution" European Conference on Computer Vision.


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