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Dec, 2023
LETA:学习可迁移的通用视觉解释器归因
LETA: Learning Transferable Attribution for Generic Vision Explainer
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Guanchu Wang, Yu-Neng Chuang, Fan Yang, Mengnan Du, Chia-Yuan Chang...
TL;DR
通过在大规模图像数据集上发展预训练的、基于深度神经网络的通用解释器,利用其可迁移性解释不同视觉模型的下游任务,我们在理论分析和实证研究中证明了其有效性。
Abstract
explainable machine learning
significantly improves the transparency of
deep neural networks
~(DNN). However, existing work is constrained to explaining the behavior of individual model predictions, and lacks the
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