Training of Generative Adversarial Networks with Hybrid Evolutionary Optimization Technique

Korde, Charudatta G.; K, Manikantta Reddy; M.H, Vasantha; B, Nithin Kumar Y.

doi:10.1109/indicon47234.2019.9030352

Cited by 10 publications

(1 citation statement)

References 12 publications

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“…Then AGGAN and E-GAN were proposed to make GANs perform better, mainly in terms of quality and diversity. These investigations, however, disregard the impact of initial conditions on GANs' training, so in the improvement of training methods, Korde et al (2019) introduced a new training GANs method based on hybrid evolutionary optimization (HEO) technology, which first utilized EAs to stabilize weights in previous generations, and then used Adam optimizer to regularize the discriminator in the remaining generations. Experiments identified that this method enhanced the training stability of GANs and achieved convincing performance in the image generation task.…”

Section: Training Of Gans With Hybrid Evolutionary Optimization (Heo)mentioning

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: Training Of Gans With Hybrid Evolutionary Optimization (Heo)mentioning

confidence: 99%