Shape biased learning using style transfer for improving accuracy of Illustration recognition

Abstract

Illustration images, such as those used as options in English exam questions, tend to have lower accuracy than photographs in object recognition using CNN, etc. It has been pointed out that CNN learns more texture than shape in object recognition, which is presumably an obstacle to improving the recognition rate of illustration images. In this study, we tried a method that inhibits the learning of texture information by synthesizing various textures for object images of the same shape utilizing style transition, promoting the learning of shape information, and confirmed the improvement of the recognition rate of illustration images.