A Proper Orthogonal Decomposition approach for parameters reduction of Single Shot Detector networks
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arXiv
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| Format: | Preprint |
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2022
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| _version_ | 1866916097194721280 |
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| author | Meneghetti, Laura Demo, Nicola Rozza, Gianluigi |
| author_facet | Meneghetti, Laura Demo, Nicola Rozza, Gianluigi |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2207_13551 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | A Proper Orthogonal Decomposition approach for parameters reduction of Single Shot Detector networks Meneghetti, Laura Demo, Nicola Rozza, Gianluigi Computer Vision and Pattern Recognition Machine Learning Numerical Analysis 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. |
| title | A Proper Orthogonal Decomposition approach for parameters reduction of Single Shot Detector networks |
| topic | Computer Vision and Pattern Recognition Machine Learning Numerical Analysis |
| url | https://arxiv.org/abs/2207.13551 |