Fetching the paper…
Reading the bibliography…
Deep convolutional neural networks (CNNs) are usually over-parameterized, which cannot be easily deployed on edge devices such as mobile phones and smart cameras.
Gradient-based learning applied to document recognition
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner · 1998
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.
Estimating or propagating gradients through stochastic neurons for conditional computation
Y. Bengio, N. Leonard, and A. C. Courville · 2013
Earlier work this paper cites.
Provable bounds for learning some deep representations
S. Arora, A. Bhaskara, R. Ge, and T. Ma · 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.
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.
Microsoft coco: Common objects in context
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick · 2014
Earlier work this paper cites.
TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mané, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viégas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng · 2015
Earlier work this paper cites.
Bayesian dark knowledge
A. K. Balan, V. Rathod, K. P. Murphy, and M. Welling · 2015
Earlier work this paper cites.
Deep learning with limited numerical precision
S. Gupta, A. Agrawal, K. Gopalakrishnan, and P. Narayanan · 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.
Fully convolutional networks for semantic segmentation
J. Long, E. Shelhamer, and T. Darrell · 2015
Cited alongside, same era.
Faster R-CNN: Towards real-time object detection with region proposal networks
S. Ren, K. He, R. Girshick, and J. Sun · 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.
Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2015
Cited alongside, same era.
92.45 on cifar-10 in torch, 2015
S. Zagoruyko · 2015
Cited alongside, same era.
Group equivariant convolutional networks
T. Cohen and M. Welling · 2016
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
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.
Channelnets: Compact and efficient convolutional neural networks via channel-wise convolutions
H. Gao, Z. Wang, and S. Ji · 2018
Later among the works it cites.
Constructing fast network through deconstruction of convolution
Y. Jeon and J. Kim · 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.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
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.
Xnor-net: Imagenet classification using binary convolutional neural networks
M. Rastegari, V. Ordonez, J. Redmon, and A. Farhadi · 2016
Cited alongside, same era.
Understanding and improving convolutional neural networks via concatenated rectified linear units
W. Shang, K. Sohn, D. Almeida, and H. Lee · 2016
Cited alongside, same era.
Convolutional neural networks with low-rank regularization
C. Tai, T. Xiao, Y. Zhang, X. Wang, et al · 2016
Cited alongside, same era.
Cnnpack: packing convolutional neural networks in the frequency domain
Y. Wang, C. Xu, S. You, D. Tao, and C. Xu · 2016
Cited alongside, same era.
M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen · 2018
Later among the works it cites.
Learning versatile filters for efficient convolutional neural networks
Y. Wang, C. Xu, C. XU, C. Xu, and D. Tao · 2018
Later among the works it cites.
Shift: A zero flop, zero parameter alternative to spatial convolutions
B. Wu, A. Wan, X. Yue, P. Jin, S. Zhao, N. Golmant, A. Gholaminejad, J. Gonzalez, and K. Keutzer · 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
Later among the works it cites.
Rethinking the value of network pruning
Z. Liu, M. Sun, T. Zhou, G. Huang, and T. Darrell · 2019
Closest in time.
Learning implicitly recurrent CNNs through parameter sharing
P. Savarese and M. Maire · 2019
Closest in time.
Slimmable neural networks
J. Yu, L. Yang, N. Xu, J. Yang, and T. Huang · 2019
Closest in time.