Improving the quality of generative models through Smirnov transformation

نویسندگان

چکیده

Solving the convergence issues of Generative Adversarial Networks (GANs) is one most outstanding problems in generative models. In this work, we propose a novel activation function to be used as output generator agent. This based on Smirnov probabilistic transformation and it specifically designed improve quality generated data. sharp contrast previous works, our provides more general approach that deals not only with replication categorical variables but any type data distribution (continuous or discrete). Moreover, derivable therefore, can seamlessly integrated backpropagation computations during GAN training processes. To validate approach, firstly evaluate proposal two different sets: a) an artificially rendered set containing mixture discrete continuous variables, b) real flow-based network traffic both normal connections cryptomining attacks. addition, three publicly available sets were added evaluation generalize obtained results. fidelity data, analyze their results terms measures statistical nature regarding use these synthetic feed nested machine learning-based classifier. The experimental evince clear outperformance Wasserstein (WGAN) tuned new respect naïve mean-based standard WGAN. allows fully substitute for classifier without significant fall accuracy.

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ژورنال

عنوان ژورنال: Information Sciences

سال: 2022

ISSN: ['0020-0255', '1872-6291']

DOI: https://doi.org/10.1016/j.ins.2022.07.066