Machine learningGenerative models
CycleGAN：具有循环一致性的非配对图像到图像翻译
CycleGAN 由 Zhu 等人在 ICCV 2017 上提出，它可以在没有配对训练样本的情况下学习在两个视觉域之间翻译图像。它同时训练两个生成器和两个判别器，强制执行循环一致性约束，使得从域 X 翻译到域 Y 再翻译回来的图像能够恢复原始图像。这使得它适用于无法获得大型对齐数据集的场景，例如将照片转换为艺术风格、将夏季风景变为冬季景象，或将卫星图像映射到地图图块。
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来源
- Zhu, J.-Y., Park, T., Isola, P., & Efros, A. A. (2017). Unpaired image-to-image translation using cycle-consistent adversarial networks. IEEE International Conference on Computer Vision (ICCV), 2242–2251. DOI: 10.1109/ICCV.2017.244 ↗
如何引用本页
ScholarGate. (2026, June 2). CycleGAN (Cycle-Consistent Image Translation). ScholarGate. https://scholargate.app/zh/deep-learning/cyclegan
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