Compressing Deep Networks Using Fisher Score of Feature Maps
Publication
, Conference
Soltani, M; Wu, S; Li, Y; Ravier, R; Ding, J; Tarokh, V
Published in: Data Compression Conference Proceedings
March 1, 2021
In this paper, we propose a new structural technique for pruning deep neural networks with skip-connections. Our approach is based on measuring the importance of feature maps in predicting the output of the model using their Fisher scores. These scores subsequently used for removing the less informative layers from the graph of the network. Extensive experiments on the classification of CIFAR-10, CIFAR-100, and SVHN data sets demonstrate the efficacy of our compressing method both in the number of parameters and operations.
Duke Scholars
Published In
Data Compression Conference Proceedings
DOI
ISSN
1068-0314
ISBN
9780738112275
Publication Date
March 1, 2021
Volume
2021-March
Start / End Page
371
Citation
APA
Chicago
ICMJE
MLA
NLM
Soltani, M., Wu, S., Li, Y., Ravier, R., Ding, J., & Tarokh, V. (2021). Compressing Deep Networks Using Fisher Score of Feature Maps. In Data Compression Conference Proceedings (Vol. 2021-March, p. 371). https://doi.org/10.1109/DCC50243.2021.00083
Soltani, M., S. Wu, Y. Li, R. Ravier, J. Ding, and V. Tarokh. “Compressing Deep Networks Using Fisher Score of Feature Maps.” In Data Compression Conference Proceedings, 2021-March:371, 2021. https://doi.org/10.1109/DCC50243.2021.00083.
Soltani M, Wu S, Li Y, Ravier R, Ding J, Tarokh V. Compressing Deep Networks Using Fisher Score of Feature Maps. In: Data Compression Conference Proceedings. 2021. p. 371.
Soltani, M., et al. “Compressing Deep Networks Using Fisher Score of Feature Maps.” Data Compression Conference Proceedings, vol. 2021-March, 2021, p. 371. Scopus, doi:10.1109/DCC50243.2021.00083.
Soltani M, Wu S, Li Y, Ravier R, Ding J, Tarokh V. Compressing Deep Networks Using Fisher Score of Feature Maps. Data Compression Conference Proceedings. 2021. p. 371.
Published In
Data Compression Conference Proceedings
DOI
ISSN
1068-0314
ISBN
9780738112275
Publication Date
March 1, 2021
Volume
2021-March
Start / End Page
371