Multi-Head Attention-based Residual Generative Adversarial Network

Generative Adversarial Network (GAN) models are powerful generative frameworks that produce realistic data through adversarial training between a generator and a discriminator. However, traditional GANs often struggle with training instability, insufficient diversity in generated samples, and loss of fine-grained details when processing complex temporal sequence data. This paper proposes a Multi-Head Attention-based Residual Generative Adversarial Network (MH-RGAN). By integrating residual structures and multi-head attention mechanisms, MH-RGAN enhances feature extraction capabilities for long sequential data. Additionally, a gradient penalty term is incorporated to enforce the Lipschitz continuity of the discriminator, thereby improving the authenticity of generated samples. In experiments, performance metrics such as accuracy, precision, recall, and F1-score were used to compare different models. Results demonstrate that MH-RGAN outperforms baseline models across all metrics. t-SNE visualizations further confirm that the generated data closely matches the distribution of real data, validating its superiority in capturing long-term dependencies in temporal sequences and generating high-quality samples.