A First Glance into Reversing Senescence on Herbarium Sample Images Through Conditional Generative Adversarial Networks

Juan Villacis-Llobet, Marco Lucio-Troya, Marvin Calvo-Navarro, Saul Calderon-Ramirez, Erick Mata-Montero

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

In this paper we describe a novel approach to perform senescense reversal on photos of leaves based on Conditional Generative Adversarial Networks, which have been used succesfully to perform similar tasks on faces of humans and other picture to picture translations. We show that their use can lead to a valid solution to this problem, as long as the task of creating a large and comprehensive dataset is surpassed. Additionally, we present a new dataset that consists of 120 paired photos of leaves manually collected for this work, in their fresh and senescenced states. We used the structure similarity index to compare the ground truth with the generated images and yielded an average of 0.9.

Original languageEnglish
Title of host publicationHigh Performance Computing - 6th Latin American Conference, CARLA 2019, Revised Selected Papers
EditorsJuan Luis Crespo-Mariño, Esteban Meneses-Rojas
PublisherSpringer
Pages438-447
Number of pages10
ISBN (Print)9783030410049
DOIs
StatePublished - 2020
Event6th Latin American High Performance Computing Conference, CARLA 2019 - Turrialba, Costa Rica
Duration: 25 Sep 201927 Sep 2019

Publication series

NameCommunications in Computer and Information Science
Volume1087 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

Conference6th Latin American High Performance Computing Conference, CARLA 2019
Country/TerritoryCosta Rica
CityTurrialba
Period25/09/1927/09/19

Keywords

  • Bioinformatics
  • Conditional-GANs
  • Herbaria
  • Senescence

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