Visualization of neural networks using saliency maps

Niels J.S. Mørch, Ulrik Kjems, Lars Kai Hansen, C. Svarer, I. Law, B. Lautrup, S. Strother, K. Rehm

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    Abstract

    The saliency map is proposed as a new method for understanding and visualizing the nonlinearities embedded in feedforward neural networks, with emphasis on the ill-posed case, where the dimensionality of the input-field by far exceeds the number of examples. Several levels of approximations are discussed. The saliency maps are applied to medical imaging (PET-scans) for identification of paradigm-relevant regions in the human brain
    Original languageEnglish
    Title of host publicationNeural Networks, 1995. Proceedings., IEEE International Conference on
    Volume4
    PublisherIEEE
    Publication date1995
    Pages2085-2090
    ISBN (Print)0-7803-2768-3
    DOIs
    Publication statusPublished - 1995
    Event1995 IEEE International Conference on Neural Networks - Perth, WA, United States
    Duration: 27 Nov 19951 Dec 1995
    http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=3505

    Conference

    Conference1995 IEEE International Conference on Neural Networks
    CountryUnited States
    CityPerth, WA
    Period27/11/199501/12/1995
    Internet address

    Bibliographical note

    Copyright: 1995 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE

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