阅读说明：本技术 使用机器学习的自动图像校正 (Automatic image correction using machine learning ) 是由 克里斯蒂安·坎顿·费勒 布莱恩·多汉斯基 托马斯·沃德·迈耶 乔纳森·莫顿 于 2017-12-28 设计创作，主要内容包括：在一个实施例中,计算系统可以访问人的训练图像和参考图像以及不完整图像。生成器可以基于不完整图像生成修复图像,并且判别器可以用于确定修复图像、训练图像和参考图像中的每一个是否可能由生成器生成。系统可以基于该确定来计算损失,并相应地更新判别器。使用更新的判别器,系统可以确定由生成器生成的第二修复图像是否可能由生成器生成。系统可以基于该确定来计算损失,并相应地更新生成器。一旦训练完成,生成器可用于生成给定图像的修改版本,例如即使人的眼睛在输入图像中是闭着的,也使人的眼睛看起来是睁开的。(In one embodiment, a computing system may access training images and reference images of a person and an incomplete image. The generator may generate a repair image based on the incomplete image, and the discriminator may be used to determine whether each of the repair image, the training image, and the reference image are likely to be generated by the generator. The system may calculate the loss based on this determination and update the arbiter accordingly. Using the updated discriminator, the system may determine whether the second repair image generated by the generator is likely to be generated by the generator. The system may calculate the loss based on the determination and update the generator accordingly. Once training is complete, the generator may be used to generate a modified version of the given image, for example to make the eyes of a person appear open even though the eyes are closed in the input image.)