Combination of Convolutional Neural Network Architecture and its Learning Method for Rotation‐Invariant Handwritten Digit Recognition
Kazuya Urazoe, Nobutaka Kuroki, Tetsuya Hirose, Masahiro Numa · IEEJ Transactions on Electrical and Electronic Engineering · 2020
This letter presents several combinations of a convolutional neural network (CNN) and its learning method for rotation‐invariant digit recognition. Rotation data augmentation is widely used for improving rotation invariance. Data augmentation commonly assigns the same label to all augmented images of the same source. However, this learning method causes some collisions between original and rotated digits. Thus, this letter presents three types of rotation‐invariance learning methods and applies them to five popular CNN architectures. Experimental results indicate that multi‐task learning on ResNet‐50 is the best combination. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.