학술논문
Sorting Convolution Operation for Achieving Rotational Invariance
Document Type
Periodical
Source
IEEE Signal Processing Letters IEEE Signal Process. Lett. Signal Processing Letters, IEEE. 31:1199-1203 2024
Subject
Language
ISSN
1070-9908
1558-2361
1558-2361
Abstract
The topic of achieving rotational invariance in convolutional neural networks (CNNs) has gained considerable attention recently, as this invariance is crucial for many computer vision tasks. In this letter, we propose a sorting convolution operation ( SConv ), which achieves invariance to arbitrary rotations without additional learnable parameters or data augmentation. It can directly replace conventional convolution operations in a classic CNN model to achieve the model's rotational invariance. Based on MNIST-rot dataset, we first analyze the impact of convolution kernel size, sampling grid and sorting method on SConv ’s rotational invariance, and compare our method with previous rotation-invariant CNN models. Then, we combine SConv with VGG, ResNet and DenseNet, and conduct classification experiments on texture and remote sensing image datasets. The results show that SConv significantly improves the performance of these models, especially when training data is limited.