Fetching the paper…
Reading the bibliography…
Acceleration of convolutional neural network has received increasing attention during the past several years.
Learning multiple layers of features from tiny images
A. Krizhevsky · 2009
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.
Rectifier nonlinearities improve neural network acoustic models
A. L. Maas, A. Y. Hannun, and A. Y. Ng · 2013
Earlier work this paper cites.
Predicting parameters in deep learning
B. Shakibi, B. Shakibi, M. Ranzato, M. Ranzato, and N. D. Freitas · 2013
Earlier work this paper cites.
Do deep nets really need to be deep?
L. J. Ba and R. Caruana · 2014
Earlier work this paper cites.
Exploiting linear structure within convolutional networks for efficient evaluation
E. Denton, W. Zaremba, J. Bruna, Y. Lecun, and R. Fergus · 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.
Do deep nets really need to be deep?
J. B. Lei and R. Caruana · 2014
Earlier work this paper cites.
Semantic image segmentation with deep convolutional nets and fully connected CRFs
L. C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille · 2015
Earlier work this paper cites.
Adam: A method for stochastic optimization
D. Kingma and J. Ba · 2015
Earlier work this paper cites.
Faster R-CNN: Towards real-time object detection with region proposal networks
S. Ren, R. Girshick, R. Girshick, and J. Sun · 2015
Earlier work this paper cites.
FitNets: Hints for thin deep nets
A. Romero, N. Ballas, S. E. Kahou, A. Chassang, C. Gatta, and Y. Bengio · 2015
Earlier work this paper cites.
ImageNet large scale visual recognition challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, and M. Bernstein · 2015
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2015
Cited alongside, same era.
Learning both weights and connections for efficient neural networks
J. Tran, J. Tran, J. Tran, and W. J. Dally · 2015
Cited alongside, same era.
Compact deep convolutional neural networks with coarse pruning
S. Anwar and W. Sung · 2016
Cited alongside, same era.
Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding
S. Han, H. Mao, and W. J. Dally · 2016
Cited alongside, same era.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Cited alongside, same era.
Network trimming: A data-driven neuron pruning approach towards efficient deep architectures
Quantization and training of neural networks for efficient integer-arithmetic-only inference
B. Jacob, S. Kligys, B. Chen, M. Zhu, M. Tang, A. Howard, H. Adam, and D. Kalenichenko · 2017
Later among the works it cites.
Pruning filters for efficient ConvNets
H. Li, A. Kadav, I. Durdanovic, H. Samet, and H. P. Graf · 2017
Later among the works it cites.
Runtime neural pruning
J. Lin, Y. Rao, J. Lu, and J. Zhou · 2017
Later among the works it cites.
Learning efficient convolutional networks through network slimming
Z. Liu, J. Li, Z. Shen, G. Huang, S. Yan, and C. Zhang · 2017
Later among the works it cites.
ThiNet: A filter level pruning method for deep neural network compression
J.-H. Luo, J. Wu, and W. Lin · 2017
Later among the works it cites.
Pruning convolutional neural networks for resource efficient inference
P. Molchanov, S. Tyree, T. Karras, T. Aila, and J. Kautz · 2017
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
H. Hu, R. Peng, Y. Tai, and C. Tang · 2016
Cited alongside, same era.
Fast ConvNets using group-wise brain damage
V. Lebedev and V. Lempitsky · 2016
Cited alongside, same era.
Stacked hourglass networks for human pose estimation
A. Newell, K. Yang, and J. Deng · 2016
Cited alongside, same era.
You only look once: Unified, real-time object detection
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi · 2016
Cited alongside, same era.
Learning structured sparsity in deep neural networks
W. Wen, C. Wu, Y. Wang, Y. Chen, and H. Li · 2016
Cited alongside, same era.
S. Zagoruyko and N. Komodakis · 2016
Cited alongside, same era.
Realtime multi-person 2d pose estimation using part affinity fields
Z. Cao, T. Simon, S.-E. Wei, and Y. Sheikh · 2017
Cited alongside, same era.
Later among the works it cites.
Automatic differentiation in PyTorch
A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer · 2017
Later among the works it cites.
Fixed-point factorized networks
P. Wang and J. Cheng · 2017
Later among the works it cites.
On compressing deep models by low rank and sparse decomposition
X. Yu, T. Liu, X. Wang, and D. Tao · 2017
Later among the works it cites.
Accelerating convolutional networks via global & dynamic filter pruning
S. Lin, R. Ji, Y. Li, Y. Wu, F. Huang, and B. Zhang · 2018
Later among the works it cites.
Exploring linear relationship in feature map subspace for ConvNets compression
D. Wang, L. Zhou, X. Zhang, X. Bai, and J. Zhou · 2018
Later among the works it cites.
Learning intrinsic sparse structures within long short-term memory
W. Wen, Y. He, S. Rajbhandari, M. Zhang, W. Wang, F. Liu, B. Hu, Y. Chen, and H. Li · 2018
Later among the works it cites.
NISP: Pruning networks using neuron importance score propagation
R. Yu, A. Li, C. F. Chen, J. H. Lai, V. I. Morariu, X. Han, M. Gao, C. Y. Lin, and L. S. Davis · 2018
Later among the works it cites.