用张量网络提升雷达图像分类的抗噪能力与模型效率
Quantum-Inspired Robust and Scalable SAR Object Classification
- 采用张量网络增强雷达图像分类的鲁棒性
- 在对抗数据污染时表现优于传统神经网络
- 适合部署在无人机等边缘设备上
SAR图像分类天然面临巨大噪声和高动态范围，尤其需要具备鲁棒性的分类模型。此外，将这些模型部署于无人机、军用飞机等边缘设备时，需在模型规模与分类精度间取得平衡。本研究探索张量网络在满足鲁棒性需求方面的潜力，特别评估其对数据投毒攻击的抵抗能力。不同于以往聚焦于传统神经网络的SAR目标检测工作，本研究重点考察张量网络在目标分类中的鲁棒性与模型压缩能力。结果表明，张量网络能有效应对噪声与模型效率双重挑战，为雷达应用及深度学习方法论提供了重要启示。
原文摘要 · Abstract (English)
SAR image classification naturally has to deal with huge noise and a high dynamic range particularly requiring robust classification models. Additionally, the deployment of these models on edge devices, such as drones and military aircraft, requires a careful balance between model size and classification accuracy. This study explores the potential of tensor networks to meet these robustness requirements, specifically evaluating their resilience to data poisoning. Unlike previous works that concentrated on conventional neural networks for SAR object detection, this research focuses on the robustness and model reduction capabilities of tensor networks in object classification. Our findings indicate that tensor networks are adept at addressing both the challenges of robustness and the need for model efficiency, thereby contributing valuable insights to the ongoing discourse in radar applications and deep learning methodologies in general.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。