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RIC-CNN: Rotation-Invariant Coordinate Convolutional Neural Network.

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Title: RIC-CNN: Rotation-Invariant Coordinate Convolutional Neural Network.
Authors: Mo, Hanlin1 (AUTHOR) hanlin.mo@oulu.fi, Zhao, Guoying1 (AUTHOR) guoying.zhao@oulu.fi
Source: Pattern Recognition. Feb2024, Vol. 146, pN.PAG-N.PAG. 1p.
Subjects: Convolutional neural networks, Image recognition (Computer vision), Data augmentation, Remote sensing
Abstract: Due to the lack of rotation invariance in traditional convolution operations, even acting a slight rotation on the input can severely degrade the performance of Convolutional Neural Networks (CNNs). To address this, we propose a Rotation-Invariant Coordinate Convolution (RIC-C), which achieves natural invariance to arbitrary rotations around the input center without additional trainable parameters or data augmentation. We first evaluate the rotational invariance of RIC-C using the MNIST dataset and compare its performance with most previous rotation-invariant CNN models. RIC-C achieves state-of-the-art classification on the MNIST-rot test set without data augmentation and with lower computational costs. Then, the interchangeability of RIC-C with traditional convolution operations is demonstrated by seamlessly integrating it into common CNN models like VGG, ResNet, and DenseNet. We conduct remote sensing image classification on the NWPU VHR-10, MTARSI and AID datasets and patch matching experiments on the UBC benchmark dataset, showing that RIC-C significantly enhances the performance of CNN models across different applications, especially when training data is limited. Our codes can be downloaded from https://github.com/HanlinMo/Rotation-Invariant-Coordinate-Convolutional-Neural-Network.git. • We propose RIC-C: a novel convolutional operation naturally invariant to all input center rotations, no extra parameters or data augmentation. • Without data augmentation, RIC-CNN shows superior performance on MNIST compared to previous rotation invariant CNNs. • RIC-C is successfully deployed to popular CNN models, and enhances their performance in various tasks, particularly with limited training data. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:Due to the lack of rotation invariance in traditional convolution operations, even acting a slight rotation on the input can severely degrade the performance of Convolutional Neural Networks (CNNs). To address this, we propose a Rotation-Invariant Coordinate Convolution (RIC-C), which achieves natural invariance to arbitrary rotations around the input center without additional trainable parameters or data augmentation. We first evaluate the rotational invariance of RIC-C using the MNIST dataset and compare its performance with most previous rotation-invariant CNN models. RIC-C achieves state-of-the-art classification on the MNIST-rot test set without data augmentation and with lower computational costs. Then, the interchangeability of RIC-C with traditional convolution operations is demonstrated by seamlessly integrating it into common CNN models like VGG, ResNet, and DenseNet. We conduct remote sensing image classification on the NWPU VHR-10, MTARSI and AID datasets and patch matching experiments on the UBC benchmark dataset, showing that RIC-C significantly enhances the performance of CNN models across different applications, especially when training data is limited. Our codes can be downloaded from https://github.com/HanlinMo/Rotation-Invariant-Coordinate-Convolutional-Neural-Network.git. • We propose RIC-C: a novel convolutional operation naturally invariant to all input center rotations, no extra parameters or data augmentation. • Without data augmentation, RIC-CNN shows superior performance on MNIST compared to previous rotation invariant CNNs. • RIC-C is successfully deployed to popular CNN models, and enhances their performance in various tasks, particularly with limited training data. [ABSTRACT FROM AUTHOR]
ISSN:00313203
DOI:10.1016/j.patcog.2023.109994