Convolutional Neural Networks (CNNs) perform very well in image
classification and object detection in recent years, but even the most advanced
models have limited rotation invariance. Known solutions include the
enhancement of training data and the increase of rotation invariance by
globally merging the rotation equivariant features. These methods either
increase the workload of training or increase the number of model parameters.
To address this problem, this paper proposes a module that can be inserted into
the existing networks, and directly incorporates the rotation invariance into
the feature extraction layers of the CNNs. This module does not have learnable
parameters and will not increase the complexity of the model. At the same time,
only by training the upright data, it can perform well on the rotated testing
set. These advantages will be suitable for fields such as biomedicine and
astronomy where it is difficult to obtain upright samples or the target has no
directionality. Evaluate our module with LeNet-5, ResNet-18 and tiny-yolov3, we
get impressive results.

本文提出一种可插入到既有卷积神经网络中的模块，直接将旋转不变性整合到 CNN 的特征提取层中，而不会增加模型复杂度，通过仅对立体数据进行训练，在旋转测试集上也能表现良好，这将适用于生物医学和天文学等难以获取垂直样本或目标没有方向性的领域，评估结果表明本模块能够有效提高 LeNet-5，ResNet-18 和 tiny-yolov3 的性能表现。