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    Creating ensembles of generative adversarial network discriminators for one-class classification

    Ermaliuc, M. and Stamate, D. and Magoulas, George and Pu, I. (2021) Creating ensembles of generative adversarial network discriminators for one-class classification. In: 22nd International Conference on Engineering Applications of Neural Networks, 25-27 June 2021, Crete, Greece (online).


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    We introduce an algorithm for one-class classification based on binary classification of the target class against synthetic samples. We use a process inspired by Generative Adversarial Networks (GANs) in order to both acquire synthetic samples and to build the one-class classifier. The first objective is achieved by leading the generator’s output into close vicinities of the target class region. For the second objective, we obtain a one-class classifier by generating an ensemble of discriminators obtained from the GAN’s training process. Our approach is tested on publicly available datasets producing promising results when compared to other methods.


    Item Type: Conference or Workshop Item (Paper)
    Keyword(s) / Subject(s): One-Class Classification, Generative Adversarial Networks
    School: School of Business, Economics & Informatics > Computer Science and Information Systems
    Research Centres and Institutes: Birkbeck Knowledge Lab
    Depositing User: George Magoulas
    Date Deposited: 10 May 2022 13:25
    Last Modified: 10 May 2022 14:08


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