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

• URL: http://arxiv.org/abs/2207.13551v1
• Date: Wed, 27 Jul 2022 14:43:14 GMT
• Title: A Proper Orthogonal Decomposition approach for parameters reduction of Single Shot Detector networks
• Authors: Laura Meneghetti and Nicola Demo and Gianluigi Rozza
• Abstract summary: We propose a dimensionality reduction framework based on Proper Orthogonal Decomposition, a classical model order reduction technique. 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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