目的 解决基于深度学习的颜色空间转换方法需求样本量大、样本采集成本高的问题。方法 以色度学和深度学习方法为基础，提出融合多通道校正的Cor−WGAN模型，设计多阶段训练方法，在小样本条件下，学习RGB到CIELab颜色空间的转换关系。先在标准颜色空间下测试模型的转换效果，再设计非标准颜色空间仿真实验和逆转换实验，并测试模型在实际应用中的效果。结果 实验结果表明，文中提出的Cor−WGAN模型的小样本学习能力较强，在64个均匀分布样本的训练条件下就可实现较好的转换效果，转换的平均色差为1.71，最小色差达到0.16，超过90%的样本点可以达到人眼无法分辨色差的转换程度。结论 文中算法在处理小样本颜色空间转换任务方面具有明显优势，为基于深度学习的色彩管理应用提供了一种新思路。

The work aims to overcome the problems that the color space conversion method based on deep learning requires a large number of samples and high cost of sample collection. Based on colorimetry and deep learning methods, a Cor-WGAN model that integrates multi-channel correction was proposed. A multi-stage training method was designed. The conversion relationship from RGB to CIELab color space under the condition of small samples was learned. First, the conversion effect of the model in the standard color space was tested. Then non-standard color space simulation experiments and inverse conversion experiments were designed to test the effect of the model in practical applications. The experimental results showed that the Cor-WGAN model proposed in this paper had strong small sample learning ability, and could achieve a good conversion effect under the training condition of 64 evenly distributed samples. The average color difference of conversion was 1.71, and the minimum color difference reached 0.16. More than 90% of the sample points could reach the conversion level that the human eye could not distinguish the color difference. The algorithm in this paper has obvious advantages in processing small sample color space conversion tasks, and provides a new idea for color management applications based on deep learning.