分阶段生成莫奈风格画作，逐步提升图像质量与细节。
A Tiered GAN Approach for Monet-Style Image Generation
- 采用多阶段层级GAN，逐级优化图像生成过程。
- 能生成基础艺术结构，但真实感和风格还原仍有不足。
- 适合对艺术风格迁移感兴趣的开发者与研究者。
生成对抗网络（GAN）在生成艺术图像方面表现出强大能力，可模仿克劳德·莫奈等著名画家的风格。本文提出一种层级式GAN模型，通过多阶段逐步精炼图像质量，将随机噪声转化为具有细节的艺术化图像。该方法结合下采样与卷积技术，有效缓解训练不稳、模式崩溃及输出质量差等问题，同时提升计算效率。实验表明，该架构能生成基础艺术结构，但与真实莫奈风格作品相比，在真实感和风格还原度上仍需改进。未来工作将聚焦于优化训练方法和模型复杂度，以缩小生成图像与真实艺术作品之间的差距。此外，本文还分析了传统GAN在艺术生成中的局限性，并提出了相应的改进策略。
原文摘要 · Abstract (English)
Generative Adversarial Networks (GANs) have proven to be a powerful tool in generating artistic images, capable of mimicking the styles of renowned painters, such as Claude Monet. This paper introduces a tiered GAN model to progressively refine image quality through a multi-stage process, enhancing the generated images at each step. The model transforms random noise into detailed artistic representations, addressing common challenges such as instability in training, mode collapse, and output quality. This approach combines downsampling and convolutional techniques, enabling the generation of high-quality Monet-style artwork while optimizing computational efficiency. Experimental results demonstrate the architecture's ability to produce foundational artistic structures, though further refinements are necessary for achieving higher levels of realism and fidelity to Monet's style. Future work focuses on improving training methodologies and model complexity to bridge the gap between generated and true artistic images. Additionally, the limitations of traditional GANs in artistic generation are analyzed, and strategies to overcome these shortcomings are proposed.
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