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arXiv · 2207.13551

A Proper Orthogonal Decomposition approach for parameters reduction of Single Shot Detector networks

Laura Meneghetti·Nicola Demo·Gianluigi Rozza
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Abstract

As a major breakthrough in artificial intelligence and deep learning, Convolutional Neural Networks have achieved an impressive success in solving many problems in several fields including computer vision and image processing. Real-time performance, robustness of algorithms and fast training processes remain open problems in these contexts. In addition object recognition and detection are challenging tasks for resource-constrained embedded systems, commonly used in the industrial sector. To overcome these issues, we propose a dimensionality reduction framework based on Proper Orthogonal Decomposition, a classical model order reduction technique, in order to gain a reduction in the number of hyperparameters of the net. We have applied such framework to SSD300 architecture using PASCAL VOC dataset, demonstrating a reduction of the network dimension and a remarkable speedup in the fine-tuning of the network in a transfer learning context.

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Laura Meneghetti, Nicola Demo, Gianluigi Rozza. 2022-07-27. A Proper Orthogonal Decomposition approach for parameters reduction of Single Shot Detector networks. https://doi.org/10.1109/icip46576.2022.9897513

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