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Although deeper and larger neural networks have achieved better performance, the complex network structure and increasing computational cost cannot meet the demands of many resource-constrained applications.
Statistical theory of extreme values and some practical applications: a series of lectures
E. J. Gumbel · 1954
Earlier work this paper cites.
Robust real-time face detection
P. Viola and M. J. Jones · 2004
Earlier work this paper cites.
ImageNet: A Large-Scale Hierarchical Image Database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
A. Krizhevsky and G. Hinton · 2009
Earlier work this paper cites.
Cascade object detection with deformable part models
P. F. Felzenszwalb, R. B. Girshick, and D. McAllester · 2010
Earlier work this paper cites.
Deep sparse rectifier neural networks
X. Glorot, A. Bordes, and Y. Bengio · 2011
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
Earlier work this paper cites.
Visualizing and understanding convolutional networks
M. D. Zeiler and R. Fergus · 2014
Earlier work this paper cites.
An exploration of parameter redundancy in deep networks with circulant projections
Y. Cheng, F. X. Yu, R. S. Feris, S. Kumar, A. Choudhary, and S.-F. Chang · 2015
Earlier work this paper cites.
S. Han, H. Mao, and W. J. Dally · 2015
Earlier work this paper cites.
Distilling the knowledge in a neural network
G. Hinton, O. Vinyals, and J. Dean · 2015
Earlier work this paper cites.
Training cnns with low-rank filters for efficient image classification
Y. Ioannou, D. Robertson, J. Shotton, R. Cipolla, and A. Criminisi · 2015
Earlier work this paper cites.
Going deeper with convolutions
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich · 2015
Earlier work this paper cites.
M. Courbariaux, I. Hubara, D. Soudry, R. El-Yaniv, and Y. Bengio · 2016
Cited alongside, same era.
Perforatedcnns: Acceleration through elimination of redundant convolutions
M. Figurnov, A. Ibraimova, D. P. Vetrov, and P. Kohli · 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.
Pruning convolutional neural networks for resource efficient inference
P. Molchanov, S. Tyree, T. Karras, T. Aila, and J. Kautz · 2016
Cited alongside, same era.
Branchynet: Fast inference via early exiting from deep neural networks
S. Teerapittayanon, B. McDanel, and H. Kung · 2016
Cited alongside, same era.
Aggregated residual transformations for deep neural networks
S. Xie, R. Girshick, P. Dollár, Z. Tu, and K. He · 2017
Later among the works it cites.
sksq96/pytorch-summary, Sep 2018
S. Chandel · 2018
Later among the works it cites.
Squeeze-and-excitation networks
J. Hu, L. Shen, and G. Sun · 2018
Later among the works it cites.
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 · 2018
Later among the works it cites.
Shufflenet v2: Practical guidelines for efficient cnn architecture design
N. Ma, X. Zhang, H.-T. Zheng, and J. Sun · 2018
Later among the works it cites.
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More is less: A more complicated network with less inference complexity
X. Dong, J. Huang, Y. Yang, and S. Yan · 2017
Cited alongside, same era.
Spatially adaptive computation time for residual networks
M. Figurnov, M. D. Collins, Y. Zhu, L. Zhang, J. Huang, D. Vetrov, and R. Salakhutdinov · 2017
Cited alongside, same era.
Channel pruning for accelerating very deep neural networks
Y. He, X. Zhang, and J. Sun · 2017
Cited alongside, same era.
Mobilenets: Efficient convolutional neural networks for mobile vision applications
A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam · 2017
Cited alongside, same era.
Multi-scale dense networks for resource efficient image classification
G. Huang, D. Chen, T. Li, F. Wu, L. van der Maaten, and K. Q. Weinberger · 2017
Cited alongside, same era.
Skipnet: Learning dynamic routing in convolutional networks
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Cited alongside, same era.
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Later among the works it cites.
Mobilenetv2: Inverted residuals and linear bottlenecks
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Later among the works it cites.
Convolutional networks with adaptive inference graphs
A. Veit and S. Belongie · 2018
Later among the works it cites.
Quantization mimic: Towards very tiny cnn for object detection
Y. Wei, X. Pan, H. Qin, W. Ouyang, and J. Yan · 2018
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Blockdrop: Dynamic inference paths in residual networks
Z. Wu, T. Nagarajan, A. Kumar, S. Rennie, L. S. Davis, K. Grauman, and R. Feris · 2018
Later among the works it cites.
J. Yu, L. Yang, N. Xu, J. Yang, and T. Huang · 2018
Later among the works it cites.
Shufflenet: An extremely efficient convolutional neural network for mobile devices
X. Zhang, X. Zhou, M. Lin, and J. Sun · 2018
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