Publications
If you use Caffe, cite the arXiv paper:
@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}
}}
This is a short paper that motivates the framework, compares it to other implementations, touches on engineering details, and highlights applications.
Caffe: Convolutional Architecture for Fast Feature Embedding
Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross
Girshick, Sergio Guadarrama, Trevor Darrell
ACM MM Open Source Competition 2014
arXiv / [acmm]
Google Scholar index for Caffe.
Tell Me What You See and I will Show You Where It Is
Jia Xu, Alexander G. Schwing, Raquel Urtasun.
CVPR 2014
Rich feature hierarchies for accurate object detection and semantic segmentation
R. Girshick, J. Donahue, T. Darrell, J. Malik
CVPR 2014
arXiv:1311.2524, November 2013.
CNN: Single-label to Multi-label
Yunchao Wei, Wei Xia, Junshi Huang, Bingbing Ni, Jian Dong, Yao Zhao, Shuicheng Yan
arXiv 2014
Speeding up Convolutional Neural Networks with Low Rank Expansions
Max Jaderberg, Andrea Vedaldi, Andrew Zisserman
BMVC 2014
Synthetic Data and Artificial Neural Networks for Natural Scene Text Recognition
Max Jaderberg, Karen Simonyan, Andrea Vedaldi, Andrew Zisserman
arXiv 2014
[Simultaneous Detection and Segmentation] (http://www.cs.berkeley.edu/~bharath2/pubs/pdfs/BharathECCV2014.pdf)
B. Hariharan, P. Arbelaez, R. Girshick, J. Malik
ECCV 2014
[Learning Rich Features from RGB-D Images for Object Detection and Segmentation] (http://www.cs.berkeley.edu/~sgupta/pdf/rcnn-depth.pdf)
S. Gupta, R. Girshick, P. Arbelaez, J. Malik
ECCV 2014
[Part-based R-CNNs for Fine-grained Category Detection]
(http://www.cs.berkeley.edu/~rbg/papers/part-rcnn.pdf)
N. Zhang, J. Donahue, R. Girshick, T. Darrell
ECCV 2014
Long-term Recurrent Convolutional Networks for Visual Recognition and Description
Jeff Donahue, Lisa Anne Hendricks, Sergio Guadarrama, Marcus Rohrbach, Subhashini Venugopalan, Kate Saenko, Trevor Darrell
arXiv 2014
Fully Convolutional Networks for Semantic Segmentation
Jonathan Long, Evan Shelhamer, Trevor Darrell
arXiv 2014
How transferable are features in deep neural networks?
Jason Yosinski, Jeff Clune, Yoshua Bengio, Hod Lipson
NIPS 2014 (see also arXiv preprint)
Learning Spatiotemporal Features with 3D Convolutional Networks
D. Tran, L. Bourdev, R. Fergus, L. Torresani, M. Paluri
ICCV 2015
Audio-Based Multimedia Event Detection with DNNs and Sparse Sampling
Khalid Ashraf, Benjamin Elizalde, Forrest Iandola, Matthew Moskewicz, Julia Bernd, Gerald Friedland, Kurt Keutzer.
International Conference on Multimedia Retrieval (ICMR), 2015.
Age and Gender Classification using Convolutional Neural Networks
Gil Levi, Tal Hassner
CVPR 2015, AMFG workshop
Deep Learning of Binary Hash Codes for Fast Image Retrieval
K. Lin, H.-F. Yang, J.-H. Hsiao, C.-S. Chen
CVPR 2015, DeepVision workshop
[Object Level Deep Feature Pooling for Compact Image Representation]
(http://arxiv.org/abs/1504.06591)
K. R. Mopuri, R. Venkatesh Babu
CVPR 2015, DeepVision workshop
Deformable Part Models are Convolutional Neural Networks
Ross Girshick, Forrest Iandola, Trevor Darrell, and Jitendra Malik
CVPR, 2015.
Emotion Recognition in the Wild via Convolutional Neural Networks and Mapped Binary Patterns
Gil Levi, Tal Hassner
ICMI 2015
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and < 1MB model size
Forrest N. Iandola, Matthew W. Moskewicz, Khalid Ashraf, Song Han, William J. Dally, and Kurt Keutzer
arXiv:1602.07360, 2016
Learning Compact Binary Descriptors with Unsupervised Deep Neural Networks
K. Lin, J. Lu, C.-S. Chen, J. Zhou
CVPR 2016
Add your own!