LarvaNet: Hierarchical Super-Resolution via Multi-exit Architecture

Geun Woo Jeon, Jun Ho Choi, Jun Hyuk Kim, Jong Seok Lee

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

2 Citations (Scopus)


In recent years, image super-resolution (SR) methods using convolutional neural networks (CNNs) have achieved successful results. Nevertheless, it is often difficult to apply them in resource-constrained environments due to the requirement of heavy computation and huge storage capacity. To address this issue, we propose an efficient network model for SR, called LarvaNet. First, we investigate a number of architectural factors for a baseline model and find optimal settings in terms of performance, number of parameters, and running time. Based on that, we design our model using a multi-exit architecture. Our experiments show that the proposed method achieves state-of-the-art SR performance with a reasonable number of parameters and running time. We also show that the multi-exit architecture of the proposed model allows us to control the trade-off between resource consumption and SR performance by selecting which exit point to be used.

Original languageEnglish
Title of host publicationComputer Vision – ECCV 2020 Workshops, Proceedings
EditorsAdrien Bartoli, Andrea Fusiello
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages14
ISBN (Print)9783030670696
Publication statusPublished - 2020
EventWorkshops held at the 16th European Conference on Computer Vision, ECCV 2020 - Glasgow, United Kingdom
Duration: 2020 Aug 232020 Aug 28

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12537 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceWorkshops held at the 16th European Conference on Computer Vision, ECCV 2020
Country/TerritoryUnited Kingdom

Bibliographical note

Funding Information:
Acknowledgement. This work was supported by the IITP grant funded by the Korea government (MSIT) (R7124-16-0004, Development of Intelligent Interaction Technology Based on Context Awareness and Human Intention Understanding) and the Artificial Intelligence Graduate School Program (Yonsei University, 2020-0-01361).

Publisher Copyright:
© 2020, Springer Nature Switzerland AG.

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


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