Quantum Gradient Class Activation Map for Model Interpretability

Autor:	Lin, Hsin-Yi, Tseng, Huan-Hsin, Chen, Samuel Yen-Chi, Yoo, Shinjae
Rok vydání:	2024
Předmět:	Quantum Physics Computer Science - Artificial Intelligence Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Quantum machine learning (QML) has recently made significant advancements in various topics. Despite the successes, the safety and interpretability of QML applications have not been thoroughly investigated. This work proposes using Variational Quantum Circuits (VQCs) for activation mapping to enhance model transparency, introducing the Quantum Gradient Class Activation Map (QGrad-CAM). This hybrid quantum-classical computing framework leverages both quantum and classical strengths and gives access to the derivation of an explicit formula of feature map importance. Experimental results demonstrate significant, fine-grained, class-discriminative visual explanations generated across both image and speech datasets. Comment: Submitted to IEEE SiPS 2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2408.05899 Zobrazit plný text záznamu View this record from Arxiv