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This paper aims to simultaneously accelerate and compress off-the-shelf CNN models via filter pruning strategy.
Learning distributed representations of concepts
G. Hinton · 1986
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Optimal brain damage
Y. LeCun, J. S. Denker, S. A. Solla, R. E. Howard, and L. D. Jackel · 1990
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Second order derivatives for network pruning: Optimal brain surgeon
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The Caltech-UCSD birds-200-2011 dataset
C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie · 2011
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Improving neural networks by preventing co-adaptation of feature detectors
G. Hinton, N. Srivastava, A. Krizhevsky, I. Sutskever, and R. R. Salakhutdinov · 2012
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Imagenet classification with deep convolutional neural networks
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Representation learning: A review and new perspectives
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M. Lin, Q. Chen, and S. Yan · 2013
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Do deep nets really need to be deep?
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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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Rich feature hierarchies for accurate object detection and semantic segmentation
R. Girshick, J. Donahue, T. Darrell, and J. Malik · 2014
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Compressing deep convolutional networks using vector quantization
Y. Gong, L. Liu, M. Yang, and L. Bourdev · 2014
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Distilling the knowledge in a neural network
G. Hinton, O. Vinyals, and J. Dean · 2014
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Learning distributed representations of concepts
Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell · 2014
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Compressing neural networks with the hashing trick
Going deeper with convolutions
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich · 2015
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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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Deep compression: Compressing deep neural network with pruning, trained quantization and huffman coding
S. Han, H. Mao, and W. J. Dally · 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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Network trimming: A data-driven neuron pruning approach towards efficient deep architectures
H. Hu, R. Peng, Y. W. Tai, and C. K. Tang · 2016
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ImageNet large scale visual recognition challenge
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Densecap: Fully convolutional localization networks for dense captioning
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Pruning filters for efficient ConvNets
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Face model compression by distilling knowledge from neurons
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XNOR-Net: ImageNet classification using binary convolutional neural networks
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Quantized convolutional neural networks for mobile devices
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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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