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Rotation-invariant NN for learning naturally un-oriented data


Deep convolutional neural networks accuracy is heavily impacted by the rotations of the input data. In this paper, we propose a convolutional predictor that is invariant to rotations in the input. This architecture is capable of predicting the angular orientation without angle-annotated data. Furthermore, the predictor maps continuously the random rotation of the input to a circular space of the prediction. For this purpose, we use the roto-translation properties existing in the Scattering Transform Networks with a series of 3D Convolutions. We validate the results by training with upright and randomly rotated samples. This allows further applications of this work on fields like automatic re-orientation of randomly oriented datasets.
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hal-02022802 , version 1 (18-02-2019)


  • HAL Id : hal-02022802 , version 1


Rosemberg Rodriguez Salas, Petr Dokládal, Eva Dokladalova. Rotation-invariant NN for learning naturally un-oriented data. 2019. ⟨hal-02022802⟩
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