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  • Spectrum-Guided Adversarial Disparity Learning

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    Wang443149-Accepted.pdf (3.186Mb)
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    Accepted Manuscript (AM)
    Author(s)
    Liu, Z
    Yao, L
    Bai, L
    Wang, X
    Wang, C
    Griffith University Author(s)
    Wang, Can
    Year published
    2020
    Metadata
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    Abstract
    It has been a significant challenge to portray intraclass disparity precisely in the area of activity recognition, as it requires a robust representation of the correlation between subject-specific variation for each activity class. In this work, we propose a novel end-to-end knowledge directed adversarial learning framework, which portrays the class-conditioned intraclass disparity using two competitive encoding distributions and learns the purified latent codes by denoising learned disparity. Furthermore, the domain knowledge is incorporated in an unsupervised manner to guide the optimization and further boosts the ...
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    It has been a significant challenge to portray intraclass disparity precisely in the area of activity recognition, as it requires a robust representation of the correlation between subject-specific variation for each activity class. In this work, we propose a novel end-to-end knowledge directed adversarial learning framework, which portrays the class-conditioned intraclass disparity using two competitive encoding distributions and learns the purified latent codes by denoising learned disparity. Furthermore, the domain knowledge is incorporated in an unsupervised manner to guide the optimization and further boosts the performance. The experiments on four HAR benchmark datasets demonstrate the robustness and generalization of our proposed methods over a set of state-of-the-art. We further prove the effectiveness of automatic domain knowledge incorporation in performance enhancement.
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    Conference Title
    KDD '20: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
    DOI
    https://doi.org/10.1145/3394486.3403054
    Copyright Statement
    © ACM, 2020. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in KDD '20: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, ISBN: 978-1-4503-7998-4, https://doi.org/10.1145/3394486.3403054
    Subject
    Artificial intelligence
    Publication URI
    http://hdl.handle.net/10072/401606
    Collection
    • Conference outputs

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