A Comparative Study on the Integration of Attention Mechanisms in GAN Architectures

To enhance the structural reconstruction capabilities and semantic consistency of generative adversarial networks (GANs) in high-resolution image generation, this study focuses on the integration methods and performance differences of various attention mechanisms within GAN architectures. A systematic analysis was conducted on four mainstream mechanismsself-attention, SE, CBAM, and non-localacross the generator, discriminator, and bidirectional embedding paths. Using the COCO and CelebA-HQ datasets, with a unified image resolution of 256256, controlled experiments were designed with parameter increases kept within 10%. Evaluation metrics included inception score, FID, PSNR, SSIM, and loss variance. The results show that self-attention and non-local modules have significant advantages in modeling long-range dependencies and global semantics, with FID reduced to 41.5 and 39.8, PSNR improved to 26.9 dB and 27.1 dB, SSIM reaching 0.834 and 0.839, and training stability metrics such as loss variance reduced to 0.049 and 0.047. In contrast, SE and CBAM achieve performance improvements with extremely low parameter growth, making them suitable for model lightweight requirements. The dual-end embedding path performed optimally across all metrics, demonstrating the effectiveness of collaborative modeling between the generator and discriminator. Analysis suggests that different attention mechanisms significantly impact model performance, with integration methods and embedding positions determining the ability to restore image details and model semantic consistency. This provides theoretical support and experimental evidence for future optimization of attention mechanism structures and the development of dynamic integration strategies.