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Very large-scale Deep Neural Networks (DNNs) have achieved remarkable successes in a large variety of computer vision tasks.
Optimal brain damage
Y. LeCun, J. S. Denker, S. A. Solla, R. E. Howard, and L. D. Jackel · 1989
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Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
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Learning multiple layers of features from tiny images
A. Krizhevsky and G. Hinton · 2009
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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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Predicting parameters in deep learning
M. Denil, B. Shakibi, L. Dinh, M. A. Ranzato, and N. de Freitas · 2013
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Exploiting linear structure within convolutional networks for efficient evaluation
E. L. Denton, W. Zaremba, J. Bruna, Y. LeCun, and R. Fergus · 2014
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Speeding up convolutional neural networks with low rank expansions
M. Jaderberg, A. Vedaldi, and A. Zisserman · 2014
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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
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Speeding-up convolutional neural networks using fine-tuned cp-decomposition
V. Lebedev, Y. Ganin, M. Rakhuba, I. Oseledets, and V. Lempitsky · 2014
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
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Visualizing and understanding convolutional networks
M. D. Zeiler and R. Fergus · 2014
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k-means clustering is matrix factorization
C. Bauckhage · 2015
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S. Han, H. Mao, and W. J. Dally · 2015
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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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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
K. He, X. Zhang, S. Ren, and J. Sun · 2015
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Learning the number of neurons in deep networks
J. M. Alvarez and M. Salzmann · 2016
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Dynamic network surgery for efficient dnns
Y. Guo, A. Yao, and Y. Chen · 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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Fast convnets using group-wise brain damage
V. Lebedev and V. Lempitsky · 2016
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Convolutional neural networks with low-rank regularization
C. Tai, T. Xiao, X. Wang, and W. E · 2016
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Accelerating convolutional neural networks for mobile applications
P. Wang and J. Cheng · 2016
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Y. Ioannou, D. P. Robertson, J. Shotton, R. Cipolla, and A. Criminisi · 2015
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Compression of deep convolutional neural networks for fast and low power mobile applications
Y.-D. Kim, E. Park, S. Yoo, T. Choi, L. Yang, and D. Shin · 2015
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Fast algorithms for convolutional neural networks
A. Lavin · 2015
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Sparse convolutional neural networks
B. Liu, M. Wang, H. Foroosh, M. Tappen, and M. Pensky · 2015
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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.
W. Wen, C. Wu, Y. Wang, Y. Chen, and H. Li · 2016
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Accelerating very deep convolutional networks for classification and detection
X. Zhang, J. Zou, K. He, and J. Sun · 2016
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Pruning filters for efficient convnets
H. Li, A. Kadav, I. Durdanovic, H. Samet, and H. P. Graf · 2017
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Faster cnns with direct sparse convolutions and guided pruning
J. Park, S. Li, W. Wen, P. T. P. Tang, H. Li, Y. Chen, and P. Dubey · 2017
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