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.

中文翻译：

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卷积神经网络体系结构及其学习方法的旋转不变手写数字识别

这封信介绍了卷积神经网络（CNN）及其用于旋转不变数字识别的学习方法的几种组合。旋转数据增强被广泛用于改善旋转不变性。数据增强通常将相同的标签分配给同一来源的所有增强图像。但是，这种学习方法会导致原始数字和旋转数字之间发生一些冲突。因此，这封信介绍了三种旋转不变性学习方法，并将其应用于五种流行的CNN架构。实验结果表明，在ResNet-50上进行多任务学习是最好的组合。©2020日本电气工程师学会。由Wiley Periodicals LLC发布。