AutoPruner: An End-to-End Trainable Filter Pruning Method for Efficient Deep Model Inference

Gradient-based learning applied to document recognition

Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner · 1998

Earlier work this paper cites.

Learning multiple layers of features from tiny images

A. Krizhevsky · 2009

Earlier work this paper cites.

The Caltech-UCSD birds-200-2011 dataset

C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie · 2011

Earlier work this paper cites.

Predicting parameters in deep learning

M. Denil, B. Shakibi, L. Dinh, and N. de Freitas · 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

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M. Jaderberg, A. Vedaldi, and A. Zisserman · 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.

Learning both weights and connections for efficient neural network

S. Han, J. Pool, J. Tran, and W. Dally · 2015

Earlier work this paper cites.

Delving deep into rectifiers: Surpassing human-level performance on imagenet classification

K. He, X. Zhang, S. Ren, 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, A. C. Berg, and F.-F. Li · 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.

Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding

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Fast convnets using group-wise brain damage

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Learning structured sparsity in deep neural networks

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Learning efficient convolutional networks through network slimming

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ThiNet: A filter level pruning method for deep neural network compression

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