Meta-Generative Adversarial Networks: A Framework for the Adaptive Construction, Optimization, and Evolution of Generative Adversarial Mechanisms
Abstract
Generative adversarial networks (GANs) establish a powerful generative learning paradigm through adversarial interactions between generators and discriminators. However, conventional GAN systems generally operate within predefined architectural spaces, adversarial objectives, optimization procedures, and training configurations. Although existing research has explored automated GAN architecture design, evolutionary optimization, meta-learning, and adaptive adversarial training, a broader question remains: can the mechanisms responsible for constructing and coordinating GANs themselves become objects of systematic learning and optimization? This paper introduces Meta-Generative Adversarial Networks (Meta-GANs) as a proposed framework for adaptive construction and optimization of generative adversarial systems. Rather than treating a GAN as a fixed learning configuration, Meta-GANs introduce a meta-level mechanism that can select, construct, combine, modify, and deactivate lower-level generative and adversarial mechanisms according to task requirements, observed training dynamics, generation quality, diversity, stability, and computational constraints. The framework distinguishes three levels: the object level, where generation and discrimination occur; the mechanism level, where architectures, objectives, optimizers, and coordination strategies are configured; and the meta level, where configuration policies are learned and updated across training episodes or tasks. A formal optimization formulation is presented to balance generation quality, diversity, stability, adaptation, and computational cost. The paper further develops a mechanism library, an adaptive control process, research propositions, and an empirical evaluation protocol. Meta-GANs are presented as a conceptual and methodological framework rather than an empirically validated algorithm. Their potential contribution lies in unifying multiple forms of GAN adaptation within a common framework whose central research question is not only how a GAN learns to generate data, but also how the mechanisms governing its own learning can be constructed and improved.
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BibTeX
@misc{zhang2026metagenerative,
title = {{Meta-Generative Adversarial Networks: A Framework for the Adaptive Construction, Optimization, and Evolution of Generative Adversarial Mechanisms}},
author = {Jincheng Zhang},
howpublished = {Zenodo},
year = {2026},
month = oct,
doi = {10.5281/zenodo.23529957},
url = {https://zenodo.org/records/23529957},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Meta-Generative Adversarial Networks: A Framework for the Adaptive Construction, Optimization, and Evolution of Generative Adversarial Mechanisms
AU - Jincheng Zhang
PY - 2026
DA - 2026/10/21
PB - Zenodo
DO - 10.5281/zenodo.23529957
UR - https://zenodo.org/records/23529957
AB - Generative adversarial networks (GANs) establish a powerful generative learning paradigm through adversarial interactions between generators and discriminators. However, conventional GAN systems generally operate within predefined architectural spaces, adversarial objectives, optimization procedures, and training configurations. Although existing research has explored automated GAN architecture design, evolutionary optimization, meta-learning, and adaptive adversarial training, a broader question remains: can the mechanisms responsible for constructing and coordinating GANs themselves become objects of systematic learning and optimization? This paper introduces Meta-Generative Adversarial Networks (Meta-GANs) as a proposed framework for adaptive construction and optimization of generative adversarial systems. Rather than treating a GAN as a fixed learning configuration, Meta-GANs introduce a meta-level mechanism that can select, construct, combine, modify, and deactivate lower-level generative and adversarial mechanisms according to task requirements, observed training dynamics, generation quality, diversity, stability, and computational constraints. The framework distinguishes three levels: the object level, where generation and discrimination occur; the mechanism level, where architectures, objectives, optimizers, and coordination strategies are configured; and the meta level, where configuration policies are learned and updated across training episodes or tasks. A formal optimization formulation is presented to balance generation quality, diversity, stability, adaptation, and computational cost. The paper further develops a mechanism library, an adaptive control process, research propositions, and an empirical evaluation protocol. Meta-GANs are presented as a conceptual and methodological framework rather than an empirically validated algorithm. Their potential contribution lies in unifying multiple forms of GAN adaptation within a common framework whose central research question is not only how a GAN learns to generate data, but also how the mechanisms governing its own learning can be constructed and improved.
ER -