Merge pull request brade31919#23 from several27/patch-1

Fix link to the paper
dinavod12 · Jun 15, 2018 · de8d958 · de8d958
2 parents feb5a1d + a59e251
commit de8d958
Showing 1 changed file with 3 additions and 3 deletions.
diff --git a/README.md b/README.md
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 # SRGAN-tensorflow
 
 ### Introduction
-This project is a tensorflow implementation of the impressive work  [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://arxiv.org/pdf/1704.02470v1.pdf). <br />
-The result is obtained following to same setting from the v5 edition of the [paper on arxiv](https://arxiv.org/pdf/1704.02470v1.pdf). However, due to limited resources, I train my network on the [RAISE dataset](http://mmlab.science.unitn.it/RAISE/) which contains 8156 high resoution images captured by good cameras. As the results showed below, the performance is close to the result presented in the paper without using the imagenet training set. <br />
+This project is a tensorflow implementation of the impressive work  [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://arxiv.org/pdf/1609.04802.pdf). <br />
+The result is obtained following to same setting from the v5 edition of the [paper on arxiv](https://arxiv.org/pdf/1609.04802.pdf). However, due to limited resources, I train my network on the [RAISE dataset](http://mmlab.science.unitn.it/RAISE/) which contains 8156 high resoution images captured by good cameras. As the results showed below, the performance is close to the result presented in the paper without using the imagenet training set. <br />
 The result on BSD100, Set14, Set5 will be reported later. The code is highly inspired by the [pix2pix-tensorflow](https://github.com/affinelayer/pix2pix-tensorflow).
 
 #### Some results:
@@ -352,4 +352,4 @@ The training process in the tensorboard should be like this
 * [C. Ledig, L. Theis, F. Huszar, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang, W. Shi, Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network, accepted at CVPR (oral), 2017.](https://arxiv.org/pdf/1609.04802.pdf)
 * [D.-T. Dang-Nguyen, C. Pasquini, V. Conotter, G. Boato, RAISE – A Raw Images Dataset for Digital Image Forensics, ACM Multimedia Systems, Portland, Oregon, March 18-20, 2015".](http://mmlab.science.unitn.it/RAISE/)
 * [Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A. Image-to-Image Translation with Conditional Adversarial Networks CVPR, 2017.](https://arxiv.org/pdf/1611.07004v1.pdf)
-* [pix2pix-tensorflow](https://github.com/affinelayer/pix2pix-tensorflow)
+* [pix2pix-tensorflow](https://github.com/affinelayer/pix2pix-tensorflow)