Evolving GAN Formulations for Higher Quality Image Synthesis

González, Santiago F.; Kant, M.; Miikkulainen, Risto

doi:10.48550/arxiv.2102.08578

Cited by 2 publications

(3 citation statements)

References 30 publications

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“…A large number of experiments on real image datasets showed that EMOCGAN is superior to advanced methods in terms of visual fidelity of appearance and target prominence. Gonzalez et al (2021) developed a rule TaylorGAN using loss function element learning to evaluate GANs. The purpose is to improve the quality of the generated image, and enhance the GANs by Taylor expansion of the loss function of the generator and the discriminator network.…”

Section: Evolved Gans In Multi-objective Optimizationmentioning

confidence: 99%

The application of evolutionary computation in generative adversarial networks (GANs): a systematic literature survey

Wang,

Zhang,

Wang

et al. 2024

Artif Intell Rev

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As a subfield of deep learning (DL), generative adversarial networks (GANs) have produced impressive generative results by applying deep generative models to create synthetic data and by performing an adversarial training process. Nevertheless, numerous issues related to the instability of training need to be urgently addressed. Evolutionary computation (EC), using the corresponding paradigm of biological evolution, overcomes these problems and improves evolutionary-based GANs’ ability to deal with real-world applications. Therefore, this paper presents a systematic literature survey combining EC and GANs. First, the basic theories of GANs and EC are analyzed and summarized. Second, to provide readers with a comprehensive view, this paper outlines the recent advances in combining EC and GANs after detailed classification and introduces each of them. These classifications include evolutionary GANs and their variants, GANs with evolutionary strategies and differential evolution, GANs combined with neuroevolution, evolutionary GANs related to different optimization problems, and applications of evolutionary GANs. Detailed information on the evaluation metrics, network structures, and comparisons of these models is presented in several tables. Finally, future directions and possible perspectives for further development are discussed.

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Section: Evolved Gans In Multi-objective Optimizationmentioning

confidence: 99%

The application of evolutionary computation in generative adversarial networks (GANs): a systematic literature survey

Wang,

Zhang,

Wang

et al. 2024

Artif Intell Rev

View full text Add to dashboard Cite

show abstract

“…Other approaches propose improving GANs by discovering customized loss functions for each of its networks. For example, TaylorGAN [9] treats the GAN losses as Taylor expansions and optimizes custom definitions through multi-objective evolution. These losses were meant to act as an alternative to traditional GAN losses such as Wasserstein loss [1] or minimax loss.…”

Section: Related Workmentioning

confidence: 99%

“…While evolutionary computation (EC) has been utilized before to help optimize and search for successful GAN architectures [28] and loss functions [9], we could not find an evolutionary system that can perform neural architecture search for a SimGAN, adjust custom loss functions, train and evaluate models across multiple objectives, and optimize hyperparameters simultaneously while being customizable to fit any data problem, including 1D data.…”

Section: Introductionmentioning

confidence: 99%

Evolving SimGANs to Improve Abnormal Electrocardiogram Classification

Wang,

Thite,

Talebi

et al. 2022

Preprint

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Machine Learning models are used in a wide variety of domains. However, machine learning methods often require a large amount of data in order to be successful. This is especially troublesome in domains where collecting real-world data is difficult and/or expensive. Data simulators do exist for many of these domains, but they do not sufficiently reflect the real world data due to factors such as a lack of real-world noise. Recently generative adversarial networks (GANs) have been modified to refine simulated image data into data that better fits the real world distribution, using the SimGAN method. [25]. While evolutionary computing has been used for GAN evolution, there are currently no frameworks that can evolve a SimGAN. In this paper we (1) extend the SimGAN method to refine one-dimensional data, (2) modify Easy Cartesian Genetic Programming (ezCGP), an evolutionary computing framework, to create SimGANs that more accurately refine simulated data, and (3) create new feature-based quantitative metrics to evaluate refined data. We also use our framework to augment an electrocardiogram (ECG) dataset, a domain that suffers from the issues previously mentioned. In particular, while healthy ECGs can be simulated there are no current simulators of abnormal ECGs. We show that by using an evolved SimGAN to refine simulated healthy ECG data to mimic real-world abnormal ECGs, we can improve the accuracy of abnormal ECG classifiers. CCS CONCEPTS• Computing methodologies → Genetic programming; Generative and developmental approaches; Neural networks.

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Evolving GAN Formulations for Higher Quality Image Synthesis

Cited by 2 publications

References 30 publications

The application of evolutionary computation in generative adversarial networks (GANs): a systematic literature survey

The application of evolutionary computation in generative adversarial networks (GANs): a systematic literature survey

Evolving SimGANs to Improve Abnormal Electrocardiogram Classification

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