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Convolutional neural networks have been widely deployed in various application scenarios.
Model compression
C. Buciluǎ, R. Caruana, and A. Niculescu-Mizil · 2006
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.
Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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.
Recurrent models of visual attention
V. Mnih, N. Heess, A. Graves, et al · 2014
Earlier work this paper cites.
Neural machine translation by jointly learning to align and translate
D. Bahdanau, K. Cho, and Y. Bengio · 2015
Earlier work this paper cites.
Binaryconnect: Training deep neural networks with binary weights during propagations
M. Courbariaux, Y. Bengio, and J.-P. David · 2015
Earlier work this paper cites.
Learning both weights and connections for efficient neural network
S. Han, J. Pool, J. Tran, and W. Dally · 2015
Earlier work this paper cites.
Deeply-supervised nets
C.-Y. Lee, S. Xie, P. Gallagher, Z. Zhang, and Z. Tu · 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.
Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2015
Earlier work this paper cites.
Impatient dnns-deep neural networks with dynamic time budgets
M. Amthor, E. Rodner, and J. Denzler · 2016
Earlier work this paper cites.
3d deeply supervised network for automatic liver segmentation from ct volumes
Q. Dou, H. Chen, Y. Jin, L. Yu, J. Qin, and P.-A. Heng · 2016
Earlier work this paper cites.
Cross modal distillation for supervision transfer
S. Gupta, J. Hoffman, and J. Malik · 2016
Earlier work this paper cites.
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
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Deep networks with stochastic depth
G. Huang, Y. Sun, Z. Liu, D. Sedra, and K. Q. Weinberger · 2016
Cited alongside, same era.
Squeezenet: Alexnet-level accuracy with 50x fewer parameters and¡ 0.5 mb model size
F. N. Iandola, S. Han, M. W. Moskewicz, K. Ashraf, W. J. Dally, and K. Keutzer · 2016
Cited alongside, same era.
Ssd: Single shot multibox detector
W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg · 2016
Cited alongside, same era.
Distillation as a defense to adversarial perturbations against deep neural networks
N. Papernot, P. McDaniel, X. Wu, S. Jha, and A. Swami · 2016
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.
Volumetric convnets with mixed residual connections for automated prostate segmentation from 3d mr images
L. Yu, X. Yang, H. Chen, J. Qin, and P.-A. Heng · 2017
Later among the works it cites.
Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer
S. Zagoruyko and N. Komodakis · 2017
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Label refinery: Improving imagenet classification through label progression
H. Bagherinezhad, M. Horton, M. Rastegari, and A. Farhadi · 2018
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Born again neural networks
T. Furlanello, Z. C. Lipton, M. Tschannen, L. Itti, and A. Anandkumar · 2018
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Stochastic downsampling for cost-adjustable inference and improved regularization in convolutional networks
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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.
Wide residual networks
S. Zagoruyko and N. Komodakis · 2016
Cited alongside, same era.
Look closer to see better: Recurrent attention convolutional neural network for fine-grained image recognition
J. Fu, H. Zheng, and T. Mei · 2017
Cited alongside, same era.
Deep pyramidal residual networks
D. Han, J. Kim, and J. Kim · 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.
J. Kuen, X. Kong, Z. Lin, G. Wang, J. Yin, S. See, and Y.-P. Tan · 2018
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Focal loss for dense object detection
T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár · 2018
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Convolutional networks with adaptive inference graphs
A. Veit and S. Belongie · 2018
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Skipnet: Learning dynamic routing in convolutional networks
X. Wang, F. Yu, Z.-Y. Dou, T. Darrell, and J. E. Gonzalez · 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
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Deep mutual learning
Y. Zhang, T. Xiang, T. M. Hospedales, and H. Lu · 2018
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Ktan: Knowledge transfer adversarial network
P. Liu, W. Liu, H. Ma, T. Mei, and M. Seok · 2019
Closest in time.
Meal: Multi-model ensemble via adversarial learning
Z. Shen, Z. He, and X. Xue · 2019
Closest in time.
Slimmable neural networks
J. Yu, L. Yang, N. Xu, J. Yang, and T. Huang · 2019
Closest in time.