This branch is merged to master, and maybe out-of-date.
This is a branch working on low-rank deep neural networks for faster evaluation. Related work is accepted in ICCV 2017.
Coordinating Filters for Faster Deep Neural Networks in ICCV 2017
@InProceedings{WWen_2017_ICCV,
author = {Wen, Wei and Xu, Cong and Wu, Chunpeng and Wang, Yandan and Chen, Yiran and Li, Hai},
title = {Coordinating Filters for Faster Deep Neural Networks},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {December},
year = {2017}
}
The source code of NIPS 2016 on structurally-sparse DNNs is in the scnn branch.
Tutorials on using python for low-rank DNNs. More details will be updated.
sfm branch is from caffe @ commit 985493e
Caffe is released under the BSD 2-Clause license. The BVLC reference models are released for unrestricted use.
Please cite Caffe in your publications if it helps your research:
@article{jia2014caffe,
Author = {Jia, Yangqing and Shelhamer, Evan and Donahue, Jeff and Karayev, Sergey and Long, Jonathan and Girshick, Ross and Guadarrama, Sergio and Darrell, Trevor},
Journal = {arXiv preprint arXiv:1408.5093},
Title = {Caffe: Convolutional Architecture for Fast Feature Embedding},
Year = {2014}
}
