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Despite deep neural networks have demonstrated extraordinary power in various applications, their superior performances are at expense of high storage and computational costs.
Optimal brain damage
Y. LeCun, J. S. Denker, S. A. Solla, R. E. Howard, and L. D. Jackel · 1990
Earlier work this paper cites.
Second order derivatives for network pruning: Optimal brain surgeon
B. Hassibi and D. G. Stork · 1993
Earlier work this paper cites.
Model compression
C. Bucila, R. Caruana, and A. Niculescu-Mizil · 2006
Earlier work this paper cites.
Correcting sample selection bias by unlabeled data
J. Huang, A. Gretton, K. M. Borgwardt, B. Schölkopf, and A. J. Smola · 2007
Earlier work this paper cites.
Visualizing data using t-sne
L. v. d. Maaten and G. Hinton · 2008
Earlier work this paper cites.
Covariate shift by kernel mean matching
A. Gretton, A. Smola, J. Huang, M. Schmittfull, K. Borgwardt, and B. Schölkopf · 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.
A theory of learning from different domains
S. Ben-David, J. Blitzer, K. Crammer, A. Kulesza, F. Pereira, and J. W. Vaughan · 2010
Earlier work this paper cites.
A kernel two-sample test
A. Gretton, K. M. Borgwardt, M. J. Rasch, B. Schölkopf, and A. Smola · 2012
Earlier work this paper cites.
Unsupervised domain adaptation by domain invariant projection
M. Baktashmotlagh, M. T. Harandi, B. C. Lovell, and M. Salzmann · 2013
Earlier work this paper cites.
Connecting the dots with landmarks: Discriminatively learning domain-invariant features for unsupervised domain adaptation
B. Gong, K. Grauman, and F. Sha · 2013
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.
Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
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.
Speeding up convolutional neural networks with low rank expansions
M. Jaderberg, A. Vedaldi, and A. Zisserman · 2014
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Deep domain confusion: Maximizing for domain invariance
E. Tzeng, J. Hoffman, N. Zhang, K. Saenko, and T. Darrell · 2014
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Learning both weights and connections for efficient neural network
S. Han, J. Pool, J. Tran, and W. Dally · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
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Learning transferable features with deep adaptation networks
M. Long, Y. Cao, J. Wang, and M. I. Jordan · 2015
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Faster R-CNN
S. Ren, K. He, R. Girshick, and J. Sun · 2015
Diversity networks
Z. Mariet and S. Sra · 2016
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XNOR-Net
M. Rastegari, V. Ordonez, J. Redmon, and A. Farhadi · 2016
Later among the works it cites.
Accelerating convolutional neural networks with dominant convolutional kernel and knowledge pre-regression
Z. Wang, Z. Deng, and S. Wang · 2016
Later among the works it cites.
Learning structured sparsity in deep neural networks
W. Wen, C. Wu, Y. Wang, Y. Chen, and H. Li · 2016
Later among the works it cites.
Dual path networks
Y. Chen, J. Li, H. Xiao, X. Jin, S. Yan, and J. Feng · 2017
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Channel pruning for accelerating very deep neural networks
Y. He, X. Zhang, and J. Sun · 2017
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Pruning filters for efficient
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ImageNet
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, and A. C. Berg · 2015
Cited alongside, same era.
Efficient and accurate approximations of nonlinear convolutional networks
X. Zhang, J. Zou, X. Ming, K. He, and J. Sun · 2015
Cited alongside, same era.
Learning the number of neurons in deep networks
J. M. Alvarez and M. Salzmann · 2016
Cited alongside, same era.
Binarized neural networks: Training deep neural networks with weights and activations constrained to +1 or -1
M. Courbariaux, I. Hubara, D. Soudry, R. El-Yaniv, and Y. Bengio · 2016
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Image style transfer using convolutional neural networks
L. A. Gatys, A. S. Ecker, and M. Bethge · 2016
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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H. Li, A. Kadav, I. Durdanovic, H. Samet, and H. P. Graf · 2017
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Demystifying neural style transfer
Y. Li, N. Wang, J. Liu, and X. Hou · 2017
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Learning efficient convolutional networks through network slimming
Z. Liu, J. Li, Z. Shen, G. Huang, S. Yan, and C. Zhang · 2017
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Pruning convolutional neural networks for resource efficient inference
P. Molchanov, S. Tyree, T. Karras, T. Aila, and J. Kautz · 2017
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Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer
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Adversarial discriminative domain adaptation
E. Tzeng, J. Hoffman, K. Saenko, and T. Darrell · 2017
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Aggregated residual transformations for deep neural networks
S. Xie, R. Girshick, P. Dollár, Z. Tu, and K. He · 2017
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A gift from knowledge distillation: Fast optimization, network minimization and transfer learning
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