IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-3-030-45529-3_13.html
   My bibliography  Save this book chapter

Zero-Shot Task Transfer

In: Domain Adaptation in Computer Vision with Deep Learning

Author

Listed:
  • Arghya Pal

    (Indian Institute of Technology)

  • Vineeth N. Balasubramanian

    (Indian Institute of Technology)

Abstract

Cognitive science has shown results on how human subjects (human children) and other mammals adapt to an entirely novel task (depth measurement) by understanding the association with already learned tasks (self-motion, shoulder movements) without receiving an explicit supervision. Motivated by such prior work, in this chapter, we present a meta-regression algorithm that regresses model parameters of zero-shot tasks from the model parameters of known tasks and the correlation of known and zero-shot tasks. The proposed method is evaluated on the Taskonomy dataset [54] considering surface normal estimation, depth estimation, room layout and camera pose estimation as zero-shot tasks. Our proposed methodology outperforms state-of-the-art models (which use ground truth) on each of our zero-shot tasks, showing promise on zero-shot task transfer. We also conducted extensive experiments to study the various choices of our methodology, as well as showed how the proposed method can also be used in transfer learning. To the best of our knowledge, this is the first such effort on zero-shot learning in the task space.

Suggested Citation

  • Arghya Pal & Vineeth N. Balasubramanian, 2020. "Zero-Shot Task Transfer," Springer Books, in: Hemanth Venkateswara & Sethuraman Panchanathan (ed.), Domain Adaptation in Computer Vision with Deep Learning, chapter 0, pages 235-256, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-45529-3_13
    DOI: 10.1007/978-3-030-45529-3_13
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:sprchp:978-3-030-45529-3_13. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.