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

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.

References in corpus (5)

• SSD: Single Shot MultiBox Detector
• A Review of Object Detection Models based on Convolutional Neural Network
• Basic Ideas and Tools for Projection-Based Model Reduction of Parametric Partial Differential Equations
• Anchor Pruning for Object Detection
• A Dimensionality Reduction Approach for Convolutional Neural Networks