주성분 분석을 잠재공간에 적용한 최소제곱 오차 이미지 생성 네트워크

Abstract

Image generation is an important area of artificial intelligence that involves creating new images from existing dataset. It involves learning the distribution of target images from randomly generated vectors. Like other deep learning models, the image generation model requires a vast refined data set to produce high-quality results. When there is little data, there is a problem that the diversity and quality of generated images are compromised. In this paper, we propose a new generative model that applies PCA to the generator of the least square error adversarial generative network that, in turn, generates high-quality images even with a small data set. Unlike the existing models that generate target data from randomly generated noise, in the proposed method the direction of the image to be generated is guided by extracting the features of the target data through PCA. The results section shows the superior performance of the proposed model against a different number of images in dataset.