Fetching the paper…
Reading the bibliography…
This paper aims to accelerate the test-time computation of convolutional neural networks (CNNs), especially very deep CNNs that have substantially impacted the computer vision community.
Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel, “Backpropagation applied to handwritten zip code recognition,” Neural computation , 1989
1989
Earlier work this paper cites.
C. R. Reeves, Modern heuristic techniques for combinatorial problems . John Wiley & Sons, Inc., 1993
1993
Earlier work this paper cites.
G. H. Golub and C. F. van Van Loan, “Matrix computations,” 1996
1996
Earlier work this paper cites.
J. C. Gower and G. B. Dijksterhuis, Procrustes problems . Oxford University Press Oxford, 2004, vol. 3
2004
Earlier work this paper cites.
Y. Takane and S. Jung, “Generalized constrained redundancy analysis,” Behaviormetrika , pp. 179–192, 2006
2006
Earlier work this paper cites.
M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman, “The PASCAL Visual Object Classes Challenge 2007 (VOC2007) Results,” 2007
2007
Earlier work this paper cites.
Y. Takane and H. Hwang, “Regularized linear and kernel redundancy analysis,” Computational Statistics & Data Analysis , pp. 394–405, 2007
2007
Earlier work this paper cites.
Y. Wang, J. Yang, W. Yin, and Y. Zhang, “A new alternating minimization algorithm for total variation image reconstruction,” SIAM Journal on Imaging Sciences , 2008
2008
Earlier work this paper cites.
V. Nair and G. E. Hinton, “Rectified linear units improve restricted boltzmann machines,” in International Conference on Machine Learning (ICML) , 2010, pp. 807–814
2010
Earlier work this paper cites.
V. Vanhoucke, A. Senior, and M. Z. Mao, “Improving the speed of neural networks on CPUs,” in Deep Learning and Unsupervised Feature Learning Workshop, NIPS 2011 , 2011
2011
Earlier work this paper cites.
Y. Gong and S. Lazebnik, “Iterative quantization: A procrustean approach to learning binary codes,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2011
2011
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in Neural Information Processing Systems (NIPS) , 2012
2012
Earlier work this paper cites.
R. Rigamonti, A. Sironi, V. Lepetit, and P. Fua, “Learning separable filters,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2013
2013
Earlier work this paper cites.
2013
Earlier work this paper cites.
M. D. Zeiler and R. Fergus, “Visualizing and understanding convolutional neural networks,” in European Conference on Computer Vision (ECCV) , 2014
2014
Earlier work this paper cites.
P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. LeCun, “Overfeat: Integrated recognition, localization and detection using convolutional networks,” in International Conference on Learning Representations (ICLR) , 2014
2014
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Spatial pyramid pooling in deep convolutional networks for visual recognition,” in European Conference on Computer Vision (ECCV) , 2014
2014
Earlier work this paper cites.
R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich feature hierarchies for accurate object detection and semantic segmentation,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2014
2014
Cited alongside, same era.
E. Denton, W. Zaremba, J. Bruna, Y. LeCun, and R. Fergus, “Exploiting linear structure within convolutional networks for efficient evaluation,” in Advances in Neural Information Processing Systems (NIPS) , 2014
2014
Cited alongside, same era.
M. Jaderberg, A. Vedaldi, and A. Zisserman, “Speeding up convolutional neural networks with low rank expansions,” in British Machine Vision Conference (BMVC) , 2014
2014
Cited alongside, same era.
2014
Cited alongside, same era.
V. Lebedev, Y. Ganin, M. Rakhuba, I. Oseledets, and V. Lempitsky, “Speeding-up convolutional neural networks using fine-tuned cp-decomposition,” in International Conference on Learning Representations (ICLR) , 2015
2015
Closest in time.
X. Zhang, J. Zou, X. Ming, K. He, and J. Sun, “Efficient and accurate approximations of nonlinear convolutional networks,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2015
2015
Closest in time.
N. Vasilache, J. Johnson, M. Mathieu, S. Chintala, S. Piantino, and Y. LeCun, “Fast convolutional nets with fbfft: A gpu performance evaluation,” in International Conference on Learning Representations (ICLR) , 2015
2015
Closest in time.
K. He and J. Sun, “Convolutional neural networks at constrained time cost,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2015
2015
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2014
Cited alongside, same era.
T. Ge, K. He, Q. Ke, and J. Sun, “Optimized product quantization,” IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) , 2014
2014
Cited alongside, same era.
K. Chatfield, K. Simonyan, A. Vedaldi, and A. Zisserman, “Return of the devil in the details: Delving deep into convolutional nets,” in British Machine Vision Conference (BMVC) , 2014
2014
Cited alongside, same era.
2014
Cited alongside, same era.
K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” in International Conference on Learning Representations (ICLR) , 2015
2015
Cited alongside, same era.
R. Girshick, “Fast R-CNN,” in IEEE International Conference on Computer Vision (ICCV) , 2015
2015
Cited alongside, same era.
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2015
2015
Cited alongside, same era.
2015
Cited alongside, same era.
A. Romero, N. Ballas, S. E. Kahou, A. Chassang, C. Gatta, and Y. Bengio, “Fitnets: Hints for thin deep nets,” in International Conference on Learning Representations (ICLR) , 2015
2015
Closest in time.
Y. Xia, K. He, P. Kohli, and J. Sun, “Sparse projections for high-dimensional binary codes,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2015
2015
Closest in time.
2015
Closest in time.
L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, “Semantic image segmentation with deep convolutional nets and fully connected crfs,” in ICLR , 2015
2015
Closest in time.
2015
Closest in time.
H. Fang, S. Gupta, F. Iandola, R. Srivastava, L. Deng, P. Dollár, J. Gao, X. He, M. Mitchell, J. Platt et al. , “From captions to visual concepts and back,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2015
2015
Closest in time.
A. Karpathy and L. Fei-Fei, “Deep visual-semantic alignments for generating image descriptions,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2015
2015
Closest in time.
X. Chen and C. L. Zitnick, “Learning a recurrent visual representation for image caption generation,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2015
2015
Closest in time.
N. Srivastava, E. Mansimov, and R. Salakhutdinov, “Unsupervised learning of video representations using lstms,” in International Conference on Machine Learning (ICML) , 2015
2015
Closest in time.
M. Ren, R. Kiros, and R. Zemel, “Image question answering: A visual semantic embedding model and a new dataset,” in ICML 2015 Deep Learning Workshop , 2015
2015
Closest in time.
M. Cimpoi, S. Maji, and A. Vedaldi, “Deep convolutional filter banks for texture recognition and segmentation,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2015
2015
Closest in time.