(The Eurographics Association, 2022) Jeon, Yongmoon; Kim, Haneol; Lee, Hyeona; Jo, Seonghoon; Kim, Jaewon; Ghosh, Abhijeet; Wei, Li-Yi
Image classification based on neural networks has been widely explored in machine learning and most research have focused on developing more efficient and accurate network models for given image dataset mostly over natural scene. However, industrial image data have different features with natural scene images in shape of target objects, background patterns, and color. Additionally, data imbalance is one of the most challenging problems to degrade classification accuracy for industrial images. This paper proposes a novel GAN-based image generation method to improve classification accuracy for defect images of OLED panels. We validate our method can synthetically generate defect images of OLED panels and classification accuracy can be improved by training minor classes with the generated defect images.