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Rotation Invariant Networks for Image Classification for HPC and Embedded Systems

Abstract : Convolutional Neural Network (CNNs) models’ size reduction has recently gained interest due to several advantages: energy cost reduction, embedded devices, and multi-core interfaces. One possible way to achieve model reduction is the usage of Rotation-invariant Convolutional Neural Networks because of the possibility of avoiding data augmentation techniques. In this work, we present the next step to obtain a general solution to endowing CNN architectures with the capability of classifying rotated objects and predicting the rotation angle without data-augmentation techniques. The principle consists of the concatenation of a representation mapping transforming rotation to translation and a shared weights predictor. This solution has the advantage of admitting different combinations of various basic, existing blocks. We present results obtained using a Gabor-filter bank and a ResNet feature backbone compared to previous other solutions. We also present the possibility to select between parallelizing the network in several threads for energy-aware High Performance Computing (HPC) applications or reducing the memory footprint for embedded systems. We obtain a competitive error rate on classifying rotated MNIST and outperform existing state-of-the-art results on CIFAR-10 when trained on up-right examples and validated on random orientations.
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Soumis le : mardi 12 janvier 2021 - 09:12:58
Dernière modification le : samedi 22 octobre 2022 - 05:12:53


Distributed under a Creative Commons Paternité 4.0 International License

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Rosemberg Rodriguez Salas, Petr Dokládal, Eva Dokladalova. Rotation Invariant Networks for Image Classification for HPC and Embedded Systems. Electronics, 2021, 10 (2), pp.139. ⟨10.3390/electronics10020139⟩. ⟨hal-03106734⟩



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