mlsanityData to 14 Oct 2026

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

arXiv 2207.13551 ↗First posted 27 Jul 2022cs.CV cs.LG cs.NAcited by 0

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 19 of its 36 references that are in the map. First posted on arXiv 27 Jul 2022.

Builds on

19 of 36 references.

References · 19

  1. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  2. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,639
  3. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,852
  4. 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,795
  5. 2014Going Deeper with Convolutionscited by 4,241
  6. 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,845
  7. 2015SSD: Single Shot MultiBox Detectorcited by 3,130
  8. 2015Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Codingcited by 2,329
  9. 2016YOLO9000: Better, Faster, Strongercited by 1,431
  10. 2017ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devicescited by 1,200
  11. 2016SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model sizecited by 1,060
  12. 2018Object Detection with Deep Learning: A Reviewcited by 528
7 more
  1. 2018Deep Learning for Generic Object Detection: A Surveycited by 392
  2. 2016SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Drivingcited by 55
  3. 2019A Review of Object Detection Models based on Convolutional Neural Networkcited by 22
  4. 2019Active Subspace of Neural Networks: Structural Analysis and Universal Attackscited by 12
  5. 2018Tiny SSD: A Tiny Single-shot Detection Deep Convolutional Neural Network for Real-time Embedded Object Detectioncited by 8
  6. 2021Anchor Pruning for Object Detectioncited by 5
  7. 2021A Dimensionality Reduction Approach for Convolutional Neural Networkscited by 2

Built on by

Not cited yet.