An Image Classification Model that Learns MNIST Image Features and Numerical Information

In this study, we focused on the differences between image features and meta-information. For example, "0" and "9" are relatively similar in terms of image characteristics, but there is significant difference in terms of the numbers they actually mean. In contrast, "3" and "4" are relatively dissimilar in terms of image features, but the difference is small in terms of the values they actually mean. Therefore, we propose a method to learn not only the features of the image but also the actual meaning of the values. As a result, we were able to reduce the error.