论文标题
一种适当的正交分解方法，用于减少单个镜头网络的参数
A Proper Orthogonal Decomposition approach for parameters reduction of Single Shot Detector networks
论文作者
论文摘要
作为人工智能和深度学习的重大突破，卷积神经网络在解决包括计算机视觉和图像处理在内的多个领域的许多问题方面取得了令人印象深刻的成功。在这些情况下，实时性能，算法的鲁棒性和快速训练过程仍然是空旷的问题。另外，对象识别和检测是在工业部门常用的资源约束嵌入式系统的具有挑战性的任务。为了克服这些问题，我们提出了一个基于正交分解（一种经典模型订购技术）的降低降低框架，以减少网络的超参数数量。我们使用Pascal VOC数据集将这种框架应用于SSD300体系结构，证明了网络维度的降低，并且在转移学习环境中对网络的微调进行了显着的加速。
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.
