Empower parameterized generative adversarial networks using a novel particle swarm optimizer: algorithms and applications

Lulu Tian,Yuhua Cheng,Zidong Wang,Weibo Liu,Fuad E Alsaadi,Xiaohui Liu

doi:10.1007/s13042-021-01440-3

Abstract

In this paper, a novel parameterized generative adversarial network (GAN) is proposed where the parameters are introduced to enhance the performance of image segmentation. The developed algorithm is applied to the image-based crack detection problem on the thermal data obtained through the non-destructive testing process. A new regularization term, which contains three tunable hyperparameters, embedded into the objective function of the GAN in order to improve the contrast ratio of certain areas of the image so as to benefit the crack detection process. To automate the selection of the optimal hyperparameters of the GAN, a new particle swarm optimization (PSO) algorithm is put forward where a neighborhood-based velocity updating strategy is developed for the purpose of thoroughly exploring the problem space. The proposed PSO-based GAN algorithm is shown to 1) work well in detecting cracks on the thermal data generated by the eddy current pulsed thermography technique; and 2) outperforms other conventional GAN algorithms.

Highlights

The past few years have witnessed the rapid development of deep learning techniques owing to their practical application insights [45, 47, 48, 51]
Some representative generative adversarial network (GAN) variants have been proposed by introducing regularization terms into the loss function, such as the information maximizing GAN (InfoGAN) [3], and the latent optimization for GAN (LOGAN) [44]
The developed PSO-based GAN is exploited for the crack detection of the eddy current pulsed thermography (ECPT) non-destructive testing (NDT) images

Summary

Introduction

The past few years have witnessed the rapid development of deep learning techniques owing to their practical application insights [45, 47, 48, 51]. Serving as a popular deep learning algorithm, the generative adversarial network (GAN) has been successfully applied to a wide range of applications (e.g., image processing, image super-resolution, and image synthesis) because of its strong abilities in data generation and feature extraction [23, 42]. It has been widely applied to various real-world applications, the original GAN suffers from the collapse problem. To improve the generalization ability of the GAN and alleviate the collapse problem, considerable effort has been devoted to developing GAN variants [3, 8, 43, 44, 54]. Some representative GAN variants have been proposed by introducing regularization terms into the loss function, such as the information maximizing GAN (InfoGAN) [3], and the latent optimization for GAN (LOGAN) [44]. The InfoGAN has been proposed in [3] where the mutual information has

Objectives

Methods

Results

Conclusion