Efficient Image Super Resolution Via Channel Discriminative Deep Neural Network Pruning

Zejiang Hou, Sun Yuan Kung

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Deep convolutional neural networks (CNN) have demonstrated superior performance in image super-resolution (SR) problem. However, CNNs are known to be heavily over-parameterized, and suffer from abundant redundancy. The growing size of CNNs may be incompatible with their deployment on mobile or embedded devices. Network pruning has benefited classification tasks by removing redundant parameters and associated computation. However, it has rarely been studied for SR, because existing methods assume the channel-wise features are of equal importance to the final reconstruction. On the contrary, we show the existence of uninformative feature-maps with no contribution to the task. In order to identify and remove such uninformative channels, we propose a new pruning criterion, Discriminant Information, by characterizing the dependency of the output w.r.t to the hidden-layer feature-maps. Empirically, our DI-based channel pruning algorithm is able to trim the state-of-the-art SR networks significantly (e.g. 8.7x model size compression and 3.6x CPU acceleration on SRResNet), with no quantitative or visual performance loss.

Original languageEnglish (US)
Title of host publication2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3647-3651
Number of pages5
ISBN (Electronic)9781509066315
DOIs
StatePublished - May 2020
Event2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Barcelona, Spain
Duration: May 4 2020May 8 2020

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2020-May
ISSN (Print)1520-6149

Conference

Conference2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020
CountrySpain
CityBarcelona
Period5/4/205/8/20

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

Keywords

  • channel discriminativeness
  • discriminant information
  • Efficient super-resolution
  • neural network pruning

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  • Cite this

    Hou, Z., & Kung, S. Y. (2020). Efficient Image Super Resolution Via Channel Discriminative Deep Neural Network Pruning. In 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings (pp. 3647-3651). [9054019] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2020-May). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP40776.2020.9054019