Fetching the paper…
Reading the bibliography…
In this paper, we present a novel and general network structure towards accelerating the inference process of convolutional neural networks, which is more complicated in network structure yet with less inference complexity.
Learning multiple layers of features from tiny images
A. Krizhevsky and G. Hinton · 2009
Earlier work this paper cites.
The pascal visual object classes (voc) challenge
M. Everingham, L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman · 2010
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
Earlier work this paper cites.
Some improvements on deep convolutional neural network based image classification
A. G. Howard · 2013
Earlier work this paper cites.
Return of the devil in the details: Delving deep into convolutional nets
K. Chatfield, K. Simonyan, A. Vedaldi, and A. Zisserman · 2014
Earlier work this paper cites.
Training deep neural networks with low precision multiplications
M. Courbariaux, J.-P. David, and Y. Bengio · 2014
Earlier work this paper cites.
Exploiting linear structure within convolutional networks for efficient evaluation
E. L. Denton, W. Zaremba, J. Bruna, Y. LeCun, and R. Fergus · 2014
Earlier work this paper cites.
Spatially-sparse convolutional neural networks
B. Graham · 2014
Earlier work this paper cites.
Distilling the knowledge in a neural network
G. Hinton, O. Vinyals, and J. Dean · 2014
Earlier work this paper cites.
Speeding up convolutional neural networks with low rank expansions
M. Jaderberg, A. Vedaldi, and A. Zisserman · 2014
Earlier work this paper cites.
Caffe: Convolutional architecture for fast feature embedding
Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell · 2014
Earlier work this paper cites.
Network in network
M. Lin, Q. Chen, and S. Yan · 2014
Earlier work this paper cites.
On the number of linear regions of deep neural networks
G. F. Montufar, R. Pascanu, K. Cho, and Y. Bengio · 2014
Earlier work this paper cites.
Binaryconnect: Training deep neural networks with binary weights during propagations
M. Courbariaux, Y. Bengio, and J.-P. David · 2015
Cited alongside, same era.
Deep learning with limited numerical precision
S. Gupta, A. Agrawal, K. Gopalakrishnan, and P. Narayanan · 2015
Cited alongside, same era.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
Cited alongside, same era.
Sparse convolutional neural networks
B. Liu, M. Wang, H. Foroosh, M. Tappen, and M. Pensky · 2015
Cited alongside, same era.
Fitnets: Hints for thin deep nets
A. Romero, N. Ballas, S. E. Kahou, A. Chassang, C. Gatta, and Y. Bengio · 2015
Cited alongside, same era.
Imagenet large scale visual recognition challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, et al · 2015
Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding
S. Han, H. Mao, and W. J. Dally · 2016
Later among the works it cites.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Later among the works it cites.
Identity mappings in deep residual networks
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Later among the works it cites.
Quantized neural networks: Training neural networks with low precision weights and activations
I. Hubara, M. Courbariaux, D. Soudry, R. El-Yaniv, and Y. Bengio · 2016
Later among the works it cites.
Squeezenet: Alexnet-level accuracy with 50x fewer parameters and¡ 1mb model size
F. N. Iandola, M. W. Moskewicz, K. Ashraf, S. Han, W. J. Dally, and K. Keutzer · 2016
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2015
Cited alongside, same era.
Going deeper with convolutions
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich · 2015
Cited alongside, same era.
Deep fried convnets
Z. Yang, M. Moczulski, M. Denil, N. de Freitas, A. Smola, L. Song, and Z. Wang · 2015
Cited alongside, same era.
Accelerating very deep convolutional networks for classification and detection
X. Zhang, J. Zou, K. He, and J. Sun · 2015
Cited alongside, same era.
Efficient and accurate approximations of nonlinear convolutional networks
X. Zhang, J. Zou, X. Ming, K. He, and J. Sun · 2015
Cited alongside, same era.
Binarynet: Training deep neural networks with weights and activations constrained to+ 1 or-1
M. Courbariaux and Y. Bengio · 2016
Cited alongside, same era.
Fast convnets using group-wise brain damage
V. Lebedev and V. Lempitsky · 2016
Later among the works it cites.
Xnor-net: Imagenet classification using binary convolutional neural networks
M. Rastegari, V. Ordonez, J. Redmon, and A. Farhadi · 2016
Later among the works it cites.
Compression of deep neural networks on the fly
G. Soulié, V. Gripon, and M. Robert · 2016
Later among the works it cites.
Quantized convolutional neural networks for mobile devices
J. Wu, C. Leng, Y. Wang, Q. Hu, and J. Cheng · 2016
Later among the works it cites.
S. Zagoruyko and N. Komodakis · 2016
Later among the works it cites.
Less is more: Towards compact cnns
H. Zhou, J. M. Alvarez, and F. Porikli · 2016
Later among the works it cites.
Dorefa-net: Training low bitwidth convolutional neural networks with low bitwidth gradients
S. Zhou, Y. Wu, Z. Ni, X. Zhou, H. Wen, and Y. Zou · 2016
Later among the works it cites.
Pruning filters for efficient convnets
H. Li, A. Kadav, I. Durdanovic, H. Samet, and H. P. Graf · 2017
Closest in time.