Quantum-Inspired Robust and Scalable SAR Object Classification
Maximilian Scharf, Marco Trenti, Felix Bock, Padraig Davidson, Tobias Brosch + 3 more
TLDR
This paper introduces quantum-inspired tensor networks for robust and efficient SAR object classification, demonstrating resilience to data poisoning.
Key contributions
- Proposes quantum-inspired tensor networks for robust SAR object classification.
- Demonstrates tensor network resilience to data poisoning attacks.
- Achieves high accuracy and model efficiency for edge device deployment.
Why it matters
SAR image classification demands robust, efficient models for edge devices, particularly against noise and data poisoning. This paper demonstrates quantum-inspired tensor networks offer superior robustness and model efficiency, significantly advancing radar applications.
Original Abstract
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.
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