Generative Adversarial Networks (GANs) for Image Synthesis
Abstract
This work describes an improved image synthesis framework with Generative Adversarial Networks (GANs) to overcome some limitations of existing models including low-resolution output, mode collapse, and weak generalization. Through hybrid GAN-diffusion architecture, fairness-aware loss functions and multi-domain datasets, the proposed model enhances the generation quality, diversity and efficiency. We condition these samples to the task distribution and domain in the application space using explainable latent control, debiasing, and application specific conditioning to guarantee realism and usability across different domains. As verified by comparisons with FID, IS and LPIPS, we achieve competitively performance to other generative models, and this work is expected to be a new benchmark in robust and scalable image generation.
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