22nd EANN 2021, 25 - 27 June 2021, Greece

Creating Ensembles of Generative Adversarial Network Discriminators for One-class Classification

Mihai Ermaliuc, Daniel Stamate, George Magoulas, Ida Pu


  We introduce an algorithm for one-class classification based on binary classifi-cation 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 com-pared to other methods.  

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