RED-NN: Rotation-Equivariant Deep Neural Network for Classification and Prediction of Rotation

In this work, we propose a new Convolutional Neural Network (CNN) for classification of rotated objects. This architecture is built around an ordered ensemble of oriented edge detectors to create a roto-translational space that transforms the input rotation into translation. This space allows the subsequent predictor to learn the internal spatial and angular relations of the objects regardless of their orientation. No data augmentation is needed and the model remains significantly smaller. It presents a self-organization capability and learns to predict the class and the rotation angle without requiring an angle-labeled dataset. We present the results of training with both upright and randomly rotated datasets. The accuracy outperforms the current state of the art on upright oriented training