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.
