Rotation Invariant Networks for Image Classification for HPC and Embedded Systems

Electronics

Petr Dokladal¹  Rosemberg Rodriguez Salas²  Eva Dokladalova² 

[1] Center for Mathematical Morphology, MINES Paris—PSL Research University, 77300 Fontainebleau, France;LIGM, University Gustave Eiffel, CNRS, ESIEE Paris, F-77454 Marne-la-Vallée, France;

关键词: CNN;    classification;    rotation invariance;    angular prediction;    model reduction;   

DOI  :  10.3390/electronics10020139

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown