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We present a filter correlation based model compression approach for deep convolutional neural networks.
Optimal brain damage
Y. LeCun, J. S. Denker, and S. A. Solla · 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.
Gradient-based learning applied to document recognition
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner · 1998
Earlier work this paper cites.
Detection of traffic signs in real-world images: The German Traffic Sign Detection Benchmark
S. Houben, J. Stallkamp, J. Salmen, M. Schlipsing, and C. Igel · 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.
Speeding up convolutional neural networks with low rank expansions
M. Jaderberg, A. Vedaldi, and A. Zisserman · 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.
Compressing neural networks with the hashing trick
W. Chen, J. Wilson, S. Tyree, K. Weinberger, and Y. Chen · 2015
Earlier work this paper cites.
Faster r-cnn: Towards real-time object detection with region proposal networks
S. Ren, K. He, R. Girshick, and J. Sun · 2015
Earlier work this paper cites.
Imagenet large scale visual recognition challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, et al · 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.
Efficient and accurate approximations of nonlinear convolutional networks
X. Zhang, J. Zou, X. Ming, K. He, and J. Sun · 2015
Earlier work this paper cites.
Learning the number of neurons in deep networks
J. M. Alvarez and M. Salzmann · 2016
Earlier work this paper cites.
Reducing overfitting in deep networks by decorrelating representations
M. Cogswell, F. Ahmed, R. Girshick, L. Zitnick, and D. Batra · 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
Earlier work this paper cites.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Earlier work this paper cites.
Network trimming: A data-driven neuron pruning approach towards efficient deep architectures
H. Hu, R. Peng, Y.-W. Tai, and C.-K. Tang · 2016
Earlier work this paper cites.
Fast convnets using group-wise brain damage
V. Lebedev and V. Lempitsky · 2016
Earlier work this paper cites.
Ssd: Single shot multibox detector
W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg · 2016
Earlier work this paper cites.
Xnor-net: Imagenet classification using binary convolutional neural networks
M. Rastegari, V. Ordonez, J. Redmon, and A. Farhadi · 2016
Earlier work this paper cites.
Learning structured sparsity in deep neural networks
W. Wen, C. Wu, Y. Wang, Y. Chen, and H. Li · 2016
Earlier work this paper cites.
Less is more: Towards compact cnns
H. Zhou, J. M. Alvarez, and F. Porikli · 2016
Earlier work this paper cites.
Structural compression of convolutional neural networks based on greedy filter pruning
R. Abbasi-Asl and B. Yu · 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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H. Li, A. Kadav, I. Durdanovic, H. Samet, and H. P. Graf · 2017
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Feature pyramid networks for object detection
T.-Y. Lin, P. Dollár, R. B. Girshick, K. He, B. Hariharan, and S. J. Belongie · 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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Bayesian compression for deep learning
C. Louizos, K. Ullrich, and M. Welling · 2017
Amc: Automl for model compression and acceleration on mobile devices
Y. He, J. Lin, Z. Liu, H. Wang, L.-J. Li, and S. Han · 2018
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Bayesian model-agnostic meta-learning
T. Kim, J. Yoon, O. Dia, S. Kim, Y. Bengio, and S. Ahn · 2018
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J.-H. Luo, H. Zhang, H.-Y. Zhou, C.-W. Xie, J. Wu, and W. Lin · 2018
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Few-shot image recognition by predicting parameters from activations
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S. Reed, Y. Chen, T. Paine, A. van den Oord, S. M. A. Eslami, D. Rezende, O. Vinyals, and N. de Freitas · 2018
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