Two-flow Feedback Multi-scale Progressive Generative Adversarial Network

• URL: http://arxiv.org/abs/2508.16089v1
• Date: Fri, 22 Aug 2025 04:59:08 GMT
• Title: Two-flow Feedback Multi-scale Progressive Generative Adversarial Network
• Authors: Sun Weikai, Song Shijie, Chi Wenjie,
• Abstract summary: We propose a novel two-flow feedback multi-scale progressive generative adversarial network (MSPGSEN) for GAN models.<n>MSPG-SEN improves image quality and human visual perception on basis of retaining the advantages of the existing GAN model.<n>It also simplifies the training process and reduces the training cost of GAN networks.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Although diffusion model has made good progress in the field of image generation, GAN\cite{huang2023adaptive} still has a large development space due to its unique advantages, such as WGAN\cite{liu2021comparing}, SSGAN\cite{guibas2021adaptive} \cite{zhang2022vsa} \cite{zhou2024adapt} and so on. In this paper, we propose a novel two-flow feedback multi-scale progressive generative adversarial network (MSPG-SEN) for GAN models. This paper has four contributions: 1) : We propose a two-flow feedback multi-scale progressive Generative Adversarial network (MSPG-SEN), which not only improves image quality and human visual perception on the basis of retaining the advantages of the existing GAN model, but also simplifies the training process and reduces the training cost of GAN networks. Our experimental results show that, MSPG-SEN has achieved state-of-the-art generation results on the following five datasets,INKK The dataset is 89.7\%,AWUN The dataset is 78.3\%,IONJ The dataset is 85.5\%,POKL The dataset is 88.7\%,OPIN The dataset is 96.4\%. 2) : We propose an adaptive perception-behavioral feedback loop (APFL), which effectively improves the robustness and training stability of the model and reduces the training cost. 3) : We propose a globally connected two-flow dynamic residual network(). After ablation experiments, it can effectively improve the training efficiency and greatly improve the generalization ability, with stronger flexibility. 4) : We propose a new dynamic embedded attention mechanism (DEMA). After experiments, the attention can be extended to a variety of image processing tasks, which can effectively capture global-local information, improve feature separation capability and feature expression capabilities, and requires minimal computing resources only 88.7\% with INJK With strong cross-task capability.

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