Neural Network Compression: Difference between revisions
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* Mozer and Smolensky (1988)<ref name="mozer1988skeletonization"></ref> use a gate for each neuron. Then the sensitivity and be estimated with the derivative w.r.t the gate. | * Mozer and Smolensky (1988)<ref name="mozer1988skeletonization"></ref> use a gate for each neuron. Then the sensitivity and be estimated with the derivative w.r.t the gate. | ||
* Karnin estimates the sensitivity by monitoring the change in weight during training. | * Karnin <ref name="karnin1990simple"></ref> estimates the sensitivity by monitoring the change in weight during training. | ||
==Factorization== | ==Factorization== | ||
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{{reflist|refs= | {{reflist|refs= | ||
<ref name="mozer1988skeletonization">Mozer, M. C., & Smolensky, P. (1988). Skeletonization: A technique for trimming the fat from a network via relevance assessment. (NeurIPS 1988). [https://proceedings.neurips.cc/paper/1988/file/07e1cd7dca89a1678042477183b7ac3f-Paper.pdf PDF]</ref> | <ref name="mozer1988skeletonization">Mozer, M. C., & Smolensky, P. (1988). Skeletonization: A technique for trimming the fat from a network via relevance assessment. (NeurIPS 1988). [https://proceedings.neurips.cc/paper/1988/file/07e1cd7dca89a1678042477183b7ac3f-Paper.pdf PDF]</ref> | ||
<ref name="karnin1990simple">Karnin, E. D. (1990). A simple procedure for pruning back-propagation trained neural networks. (IEEE TNNLS 1990). [https://ieeexplore.ieee.org/document/80236 IEEE Xplore]</ref> | |||
}} | }} | ||
Revision as of 20:45, 2 February 2021
Brief survey on neural network compression techniques.
Pruning
Sensitivity Methods
The idea here is to measure how sensitive each neuron is.
I.e., if you remove the neuron, how will it change the output?
- Mozer and Smolensky (1988)[1] use a gate for each neuron. Then the sensitivity and be estimated with the derivative w.r.t the gate.
- Karnin [2] estimates the sensitivity by monitoring the change in weight during training.
Factorization
Resources
Surveys
- Pruning algorithms a survey (1993) by Russel Reed
- A Survey of Model Compression and Acceleration for Deep Neural Networks (2017) by Cheng et al.
References
- ↑ Mozer, M. C., & Smolensky, P. (1988). Skeletonization: A technique for trimming the fat from a network via relevance assessment. (NeurIPS 1988). PDF
- ↑ Karnin, E. D. (1990). A simple procedure for pruning back-propagation trained neural networks. (IEEE TNNLS 1990). IEEE Xplore