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2017

Improved Bayesian Compression

Federici, Marco, Ullrich, Karen, Welling, Max

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Compression of Neural Networks (NN) has become a highly studied topic in recent years.

The main reason for this is the demand for industrial scale usage of NNs such as deploying them on mobile devices, storing them efficiently, transmitting them via band-limited channels and most importantly doing inference at scale.
In this work, we propose to join the Soft-Weight Sharing and Variational Dropout approaches that show strong results to define a new state-of-the-art in terms of model compression.

Improved Bayesian Compression · Around

Built on

Simplifying neural networks by soft weight-sharing
Steven J. Nowlan and Geoffrey E. Hinton · 1992
Earlier work this paper cites.
Variational Dropout and the Local Reparameterization Trick
D. P. Kingma, T. Salimans, and M. Welling · 2015
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 · 2015
Earlier work this paper cites.

Similar

Learning structured sparsity in deep neural networks
Original
Wei Wen, Chunpeng Wu, Yandan Wang, Yiran Chen, and Hai Li · 2016
Cited alongside, same era.
Soft Weight-Sharing for Neural Network Compression
K. Ullrich, E. Meeds, and M. Welling · 2017
Cited alongside, same era.

Then

Variational Dropout Sparsifies Deep Neural Networks
D. Molchanov, A. Ashukha, and D. Vetrov · 2017
Closest in time.
Bayesian compression for deep learning
Christos Louizos, Karen Ullrich, and Max Welling · 2017
Closest in time.

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