Fetching the paper…

Reading the bibliography…

2014

Caffe: Convolutional Architecture for Fast Feature Embedding

Jia, Yangqing, Shelhamer, Evan, Donahue, Jeff et al.

Original
PDF
Post on X

Understand

Caffe provides multimedia scientists and practitioners with a clean and modifiable framework for state-of-the-art deep learning algorithms and a collection of reference models.

The framework is a BSD-licensed C++ library with Python and MATLAB bindings for training and deploying general-purpose convolutional neural networks and other deep models efficiently on commodity architectures.
Caffe fits industry and internet-scale media needs by CUDA GPU computation, processing over 40 million images a day on a single K40 or Titan GPU ($\approx$ 2.5 ms per image).
By separating model representation from actual implementation, Caffe allows experimentation and seamless switching among platforms for ease of development and deployment from prototyping machines to cloud environments.

Caffe: Convolutional Architecture for Fast Feature Embedding · Around

Built on

Torch7: A MATLAB-like environment for machine learning
R. Collobert, K. Kavukcuoglu, and C. Farabet · 2011
Earlier work this paper cites.
cuda-convnet
A. Krizhevsky · 2012
Earlier work this paper cites.
ImageNet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. Hinton · 2012
Earlier work this paper cites.
Pylearn2: a machine learning research library
I. Goodfellow, D. Warde-Farley, P. Lamblin, V. Dumoulin, M. Mirza, R. Pascanu, J. Bergstra, F. Bastien, and Y. Bengio · 2013
Earlier work this paper cites.

Similar

Recognizing image style
S. Karayev, M. Trentacoste, H. Han, A. Agarwala, T. Darrell, A. Hertzmann, and H. Winnemoeller · 2013
Cited alongside, same era.
Selective search for object recognition
J. Uijlings, K. van de Sande, T. Gevers, and A. Smeulders · 2013
Cited alongside, same era.
Decaf: A deep convolutional activation feature for generic visual recognition
J. Donahue, Y. Jia, O. Vinyals, J. Hoffman, N. Zhang, E. Tzeng, and T. Darrell · 2014
Cited alongside, same era.
Rich feature hierarchies for accurate object detection and semantic segmentation
R. Girshick, J. Donahue, T. Darrell, and J. Malik · 2014
Cited alongside, same era.

Then

Open-vocabulary object retrieval
S. Guadarrama, E. Rodner, K. Saenko, N. Zhang, R. Farrell, J. Donahue, and T. Darrell · 2014
Closest in time.
Overfeat: Integrated recognition, localization and detection using convolutional networks
P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. LeCun · 2014
Closest in time.
Panda: Pose aligned networks for deep attribute modeling
N. Zhang, M. Paluri, M. Ranzato, T. Darrell, and L. Bourdev · 2014
Closest in time.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…