关键词: 样式生成对抗网络, 图像扩充, 噪声抑制, 起始分数, 弗雷歇起始距离

Abstract: Category imbalance often occurs in the field of image multi-classification， which has a negative impact on the learning and training of the classification model. It can be effectively solved by expanding the category with fewer samples. Generative adversarial network， as a newly developed neural network in recent years， can output generated samples that are very similar to real samples when trained by real image samples. According to this characteristic， this paper designs a noise-suppressed second generation style generation adversarial network 2（NS-StyleGAN2） by combining the design philosophy of the second generation style generation adversarial network （StyleGAN2） and the characteristics of fish image. NS-StyleGAN2 removes the noise input of the low-resolution layer in the StyleGAN2’s synthetic network， so as to suppress the noise weight of the low-resolution layer and make the StyleGAN2-generated samples’ detail features more close to the real samples’. 202 images of silver carp are used for training. The method proposed in this paper is superior to DCGAN， WGAN and StyleGAN2 in inception score， Frechet inception distance and kernel inception distance， which shows this method can be used for image expansion effectively.

Key words: StyleGAN （Style Generative Adversarial Networks）, image augmentation, noise suppression, IS, FID