A Method based on Evolutionary Algorithms and Channel Attention Mechanism to Enhance Cycle Generative Adversarial Network Performance for Image Translation

Abstract

A Generative Adversarial Network (GAN) can learn the relationship between two image domains and achieve unpaired image-to-image translation. One of the breakthroughs was Cycle-consistent Generative Adversarial Networks (CycleGAN), which is a popular method to transfer the content representations from the source domain to the target domain. Existing studies have gradually improved the performance of CycleGAN models by modifying the network structure or loss function of CycleGAN. However, these methods tend to suffer from training instability and the generators lack the ability to acquire the most discriminating features between the source and target domains, thus making the generated images of low fidelity and few texture details. To overcome these issues, this paper proposes a new method that combines Evolutionary Algorithms (EAs) and Attention Mechanisms to train GANs. Specifically, from an initial CycleGAN, binary vectors indicating the activation of the weights of the generators are progressively improved upon by means of an EA. At the end of this process, the best-performing configurations of generators can be retained for image generation. In addition, to address the issues of low fidelity and lack of texture details on generated images, we make use of the channel attention mechanism. The latter component allows the candidate generators to learn important features of real images and thus generate images with higher quality. The experiments demonstrate qualitatively and quantitatively that the proposed method, namely, Attention evolutionary GAN (AevoGAN) alleviates the training instability problems of CycleGAN training. In the test results, the proposed method can generate higher quality images and obtain better results than the CycleGAN training methods present in the literature, in terms of Inception Score (IS), Fréchet Inception Distance (FID) and Kernel Inception Distance (KID).

A response‐compatible ground motion generation method using physics‐guided neural networks

“…GAN (Goodfellow et al., 2014) is a generative deep learning model, mainly used in image generation, image superresolution, and image style transfer (Xue et al., 2023). GAN is composed of a generator and a discriminator.…”

A weakly supervised active learning framework for non-intrusive load monitoring

“…Signal processing [10] and Machine Learning (ML) [11][12][13] methods have been initially proposed for NILM. Deep neural networks gained wide attention in the community in many fields [14][15][16][17][18][19]. Following the work of Kelly et al [11] that proposed three deep learning-based approaches, deep neural networks (DNNs) have been widely applied in NILM achieving the state-of-the-art performance [20][21][22][23][24][25][26][27].…”