人脸转正GAN模型的高效压缩

Efficient Compression of Face Frontalization GAN Model

生成对抗网络(Generative Adversarial Network)在正面人脸图像生成方面大放异彩,生成的正面人脸极其逼真受到研究人缘的青睐。但其强大的图像生成能力源自于其训练和使用过程中巨大的计算量,GAN结构越复杂,其计算量需求,这极大地限制了其交互式部署。为增强其部署的便利性,减少GAN的计算量需求,本文提出了一种通用的压缩算法,该算法对人脸转正GAN进行了压缩,减少了GAN中生成器的推理时间和模型大小。本文的实验证明了本文算法在相较于原网络大幅减少了计算量的情况下,压缩后的GAN网络仍然保持了较好的图片质量。

GAN performed extremely well in the frontal face image generation, the generated frontal face is very realistic favored by most researches. However, its powerful image generation ability comes from the huge calculation power and storage space required, and the more complex the GAN structure, the greater the demand for computation, which greatly limits its interactive deployment applications. To enhance the convenience of its deployment and reduce the computational requirements of the GAN, the paper proposes a general compression algorithm. The algorithm compresses the GAN model size of face frontalization and reduces the inference time. The experiments in this paper show that the compressed GAN network still obtains better image quality under the condition that the computation and storage load are greatly reduced compared with the original networks.