Image Classification with Additional Non-decision Labels using Self-supervised learning and GAN

In recent years, image recognition has been improved by using machine learning to extract high accuracy. However, in most studies, it is assumed that the image input to a model has an answer. But then, when an unanswered image or an image of extremely low quality is entered into real space, the model is forced to answer and often makes a mistake. In this paper, we propose a method to discriminate between unscrutinized images using self-supervised learning and an image generation model using a subtask.