Fetching the paper…
Reading the bibliography…
The rising popularity of intelligent mobile devices and the daunting computational cost of deep learning-based models call for efficient and accurate on-device inference schemes.
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.
Improving the speed of neural networks on cpus
V. Vanhoucke, A. Senior, and M. Z. Mao · 2011
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.
Compressing deep convolutional networks using vector quantization
Y. Gong, L. Liu, M. Yang, and L. Bourdev · 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.
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.
Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 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, et al · 2015
Earlier work this paper cites.
Compressing neural networks with the hashing trick
W. Chen, J. T. Wilson, S. Tyree, K. Q. Weinberger, and Y. Chen · 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.
S. Han, H. Mao, and W. J. Dally · 2015
Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
Cited alongside, same era.
Ssd: Single shot multibox detector
W. Liu, D. Anguelov, D. Erhan, C. Szegedy, and S. Reed · 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 residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Cited alongside, same era.
Speed/accuracy trade-offs for modern convolutional object detectors
J. Huang, V. Rathod, C. Sun, M. Zhu, A. Korattikara, A. Fathi, I. Fischer, Z. Wojna, Y. Song, S. Guadarrama, et al · 2016
Cited alongside, same era.
Rethinking the inception architecture for computer vision
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna · 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.
C. Zhu, S. Han, H. Mao, and W. J. Dally · 2016
Later among the works it cites.
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
Closest in time.
Densely connected convolutional networks
G. Huang, Z. Liu, L. van der Maaten, and K. Q. Weinberger · 2017
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Binarized neural networks
I. Hubara, M. Courbariaux, D. Soudry, R. El-Yaniv, and Y. Bengio · 2016
Cited alongside, same era.
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
Cited alongside, same era.
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
Cited alongside, same era.
F. Li, B. Zhang, and B. Liu · 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.
TensorFlow Lite
Cited in the paper.
Eigen v3
G. Guennebaud, B. Jacob, et al
Cited in the paper.
Tensorflow object detection api, 2017
J. Huang, V. Rathod, D. Chow, C. Sun, and M. Zhu · 2017
Closest in time.
Extremely low bit neural network: Squeeze the last bit out with admm
C. Leng, H. Li, S. Zhu, and R. Jin · 2017
Closest in time.
Ternary neural networks with fine-grained quantization
N. Mellempudi, A. Kundu, D. Mudigere, D. Das, B. Kaul, and P. Dubey · 2017
Closest in time.
Shufflenet: An extremely efficient convolutional neural network for mobile devices
X. Zhang, X. Zhou, M. Lin, and J. Sun · 2017
Closest in time.
Incremental network quantization: Towards lossless cnns with low-precision weights
A. Zhou, A. Yao, Y. Guo, L. Xu, and Y. Chen · 2017
Closest in time.