# Achieving Rotation-Invariant Convolution via Non-Learnable Orientation Alignment Operators

arXiv: https://arxiv.org/abs/2404.11309 · HTML: https://mlsanity.com/paper/2404.11309/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 39 references that are in the map.
- First posted on arXiv 17 Apr 2024.

## Builds on (most cited first, 24 of 24)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Visualizing and Understanding Convolutional Networks](https://mlsanity.com/paper/1311.2901/) (2013)
- [Spatial Transformer Networks](https://mlsanity.com/paper/1506.02025/) (2015)
- [Group Equivariant Convolutional Networks](https://mlsanity.com/paper/1602.07576/) (2016)
- [Remote Sensing Image Scene Classification: Benchmark and State of the Art](https://mlsanity.com/paper/1703.00121/) (2017)
- [Image Segmentation Using Deep Learning: A Survey](https://mlsanity.com/paper/2001.05566/) (2020)
- [Object Detection with Deep Learning: A Review](https://mlsanity.com/paper/1807.05511/) (2018)
- [General E(2)-Equivariant Steerable CNNs](https://mlsanity.com/paper/1911.08251/) (2019)
- [Deep Learning for Generic Object Detection: A Survey](https://mlsanity.com/paper/1809.02165/) (2018)
- [Harmonic Networks: Deep Translation and Rotation Equivariance](https://mlsanity.com/paper/1612.04642/) (2016)
- [Deep Learning for Hyperspectral Image Classification: An Overview](https://mlsanity.com/paper/1910.12861/) (2019)
- [Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning](https://mlsanity.com/paper/1710.04043/) (2017)
- [Rotation equivariant vector field networks](https://mlsanity.com/paper/1612.09346/) (2016)
- [Universal Correspondence Network](https://mlsanity.com/paper/1606.03558/) (2016)
- [Polar Transformer Networks](https://mlsanity.com/paper/1709.01889/) (2017)
- [Oriented Response Networks](https://mlsanity.com/paper/1701.01833/) (2017)
- [TI-POOLING: transformation-invariant pooling for feature learning in Convolutional Neural Networks](https://mlsanity.com/paper/1604.06318/) (2016)
- [Roto-Translation Equivariant Convolutional Networks: Application to Histopathology Image Analysis](https://mlsanity.com/paper/2002.08725/) (2020)
- [Warped Convolutions: Efficient Invariance to Spatial Transformations](https://mlsanity.com/paper/1609.04382/) (2016)
- [RIC-CNN: Rotation-Invariant Coordinate Convolutional Neural Network](https://mlsanity.com/paper/2211.11812/) (2022)
- [Rotation Invariant Transformer for Recognizing Object in UAVs](https://mlsanity.com/paper/2311.02559/) (2023)

## Built on by (most cited first, 1 of 1)

- [Accelerated Rotation-Invariant Convolution for UAV Image Segmentation](https://mlsanity.com/paper/2512.08888/) (2025)

## Abstract (arXiv)

Achieving rotational invariance in deep neural networks without data augmentation is a research hotspot. Intrinsic invariance enables features to capture targets' inherent properties, enhancing deep learning performance in visual tasks. Based on various types of non-learnable operators, this paper proposes a comprehensive set of convolution operations that are natually invariant to arbitrary rotations. Unlike most prior methods, these rotation-invariant convolutions (RIConvs) have the same number of learnable parameters and a similar computational process as standard convolutions, making them interchangeable. Using the MNIST-Rot dataset, we validate their invariance across rotation angles and compare them with previous rotation-invariant CNNs, where two gradient-based RIConvs achieve state-of-the-art results. Then, we integrate RIConvs with classic CNN backbones and evaluate them on texture recognition, aircraft type recognition, and remote sensing image classification tasks. Results show that RIConvs significantly improve accuracy, particularly with limited training data, and enhance performance even with data augmentation.