Abstract
In classification tasks, the robustness against various image transformations remains a crucial property of CNN models. When acquired using the data augmentation it comes at the price of a considerable increase in training time and the risk of overfitting. Consequently, researching other ways to endow CNNs with invariance to various transformations is an intensive field of study.This paper presents a new reduced, rotation-invariant, classification model composed of two parts: a feature representation mapping and a classifier. We provide an insight into the principle and we show that the proposed model is trainable. The model we obtain is smaller and has angular prediction capabilities.We illustrate the results on the MNIST-rot and CIFAR-10 datasets. We achieve the state-of-the-art classification score on MNIST-rot, and improve by 20% the state of the art score on rotated CIFAR-10. In all cases, we can predict the rotation angle.
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