Transfer Learning using Transformation: Is Large Unlabeled Data Helpful at Segmentation?

Heejeong Lim, Seongwook Yoon, Sanghoon Sull

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

    Abstract

    We propose a simple method of transfer learning for image segmentation. Creating labeled data for deep neural network training in image segmentation is particularly expensive than other tasks. Hence, practically, the labeled data is much less than the unlabeled data. So, we introduce a method that is helpful for segmentation by using unlabeled data. Our key is the RGB-to-HSV transformation and we use it in two ways. The first way is to pre-train a network to work as an RGB-to-HSV transformer which can extract useful features, and transfer the pre-trained weights to another network for segmentation, which is one of the most common transfer learning method. The second way is to provide additional information to the segmented network by providing HSV, the output of the pre-trained network, as additional input. We performed several experiments about our proposal using Cityscapes dataset.

    Original languageEnglish
    Title of host publicationICTC 2020 - 11th International Conference on ICT Convergence
    Subtitle of host publicationData, Network, and AI in the Age of Untact
    PublisherIEEE Computer Society
    Pages387-390
    Number of pages4
    ISBN (Electronic)9781728167589
    DOIs
    Publication statusPublished - 2020 Oct 21
    Event11th International Conference on Information and Communication Technology Convergence, ICTC 2020 - Jeju Island, Korea, Republic of
    Duration: 2020 Oct 212020 Oct 23

    Publication series

    NameInternational Conference on ICT Convergence
    Volume2020-October
    ISSN (Print)2162-1233
    ISSN (Electronic)2162-1241

    Conference

    Conference11th International Conference on Information and Communication Technology Convergence, ICTC 2020
    Country/TerritoryKorea, Republic of
    CityJeju Island
    Period20/10/2120/10/23

    Bibliographical note

    Funding Information:
    ”This research was supported by the MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program(IITP-2020-2016-0-00464) supervised by the IITP(Institute for Information & communications Technology Planning & Evaluation)“

    Publisher Copyright:
    © 2020 IEEE.

    Keywords

    • HSV transformation
    • Image segmentation
    • Transfer learning

    ASJC Scopus subject areas

    • Information Systems
    • Computer Networks and Communications

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