Improving the quality of generative models through Smirnov transformation

TL;DR

This paper introduces a novel Smirnov transformation-based activation function for GANs, enhancing data quality and convergence, applicable to various data types, and validated on synthetic and real datasets with superior results.

Contribution

The paper proposes a new differentiable activation function based on Smirnov transformation, improving GAN data quality across continuous and discrete data types, and demonstrating its effectiveness on multiple datasets.

Findings

GANs with the new activation outperform standard GANs.

Generated data can replace real data for classifier training.

High-quality synthetic data preserves privacy without accuracy loss.

Abstract

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