We propose Image-Semantic-Transformation-Reconstruction-Circle(ISTRC) model,
a novel and powerful method using facenet's Euclidean latent space to
understand the images. As the name suggests, ISTRC construct the circle, able
to perfectly reconstruct images. One powerful Euclidean latent space embedded
in ISTRC is FaceNet's last layer with the power of distinguishing and
understanding images. Our model will reconstruct the images and manipulate
Euclidean latent vectors to achieve semantic transformations and semantic
images arthimetic calculations. In this paper, we show that ISTRC performs 10
high-level semantic transformations like "Male and female","add smile","open
mouth", "deduct beard or add mustache", "bigger/smaller nose", "make older and
younger", "bigger lips", "bigger eyes", "bigger/smaller mouths" and "more
attractive". It just takes 3 hours(GTX 1080) to train the models of 10 semantic
transformations.

本文提出了一种名为 ISTRC 的模型，它使用 Facenet 的欧几里得潜空间来理解图像，能够实现完美重构，以实现语义变换和图像算术计算。ISTRC 可以执行包括男女性别、微笑、大嘴巴、减掉胡须或加上胡须、变大 / 变小的鼻子、老化及年轻化、变大的嘴唇、变大 / 变小的眼睛和嘴巴、更有吸引力等十种高级语义变换。训练模型只需要 3 小时。