文件列表:

DeepImageSynthesis (0, 2016-03-07)
DeepImageSynthesis\ImageSyn.py (2803, 2016-03-07)
DeepImageSynthesis\LossFunctions.py (3188, 2016-03-07)
DeepImageSynthesis\Misc.py (7285, 2016-03-07)
DeepImageSynthesis\__init__.py (71, 2016-03-07)
Example.ipynb (653679, 2016-03-07)
Images (0, 2016-03-07)
Images\pebbles.jpg (31997, 2016-03-07)
Models (0, 2016-03-07)
Models\VGG_ave_pool_deploy.prototxt (4711, 2016-03-07)

# DeepTextures Code to synthesise textures using convolutional neural networks as described in the paper "Texture Synthesis Using Convolutional Neural Networks" (Gatys et al., NIPS 2015) (http://arxiv.org/abs/1505.07376). More examples of synthesised textures can be found at http://bethgelab.org/deeptextures/. The IPythonNotebook Example.ipynb contains the code to synthesise the pebble texture shown in Figure 3A (177k parameters) of the revised version of the paper. In the notebook I additionally match the pixel histograms in each colorchannel of the synthesised and original texture, which is not done in the figures in the paper. #Prerequisites * To run the code you need a recent version of the [Caffe](https://github.com/BVLC/caffe) deep learning framework and its dependencies (tested with master branch at commit 20c474fe40fe43dee68545dc80809f30ccdbf99b). * The images in the paper were generated using a normalised version of the [19-layer VGG-Network](http://www.robots.ox.ac.uk/~vgg/research/very_deep/) described in the work by [Simonyan and Zisserman](http://arxiv.org/abs/1409.1556). The weights in the normalised network are scaled such that the mean activation of each filter over images and positions is equal to 1. **The normalised network can be downloaded [here](http://bethgelab.org/media/uploads/deeptextures/vgg_normalised.caffemodel) and has to be copied into the Models/ folder.** # Disclaimer This software is published for academic and non-commercial use only.

近期下载者：

相关文件：

评论：[我要评论] [举报此文件]

收藏者：