可解释性
人工智能
计算机科学
特征(语言学)
卷积神经网络
情态动词
先验概率
深度学习
网络体系结构
特征学习
机器学习
模式识别(心理学)
化学
贝叶斯概率
哲学
语言学
高分子化学
计算机安全
作者
Xin Deng,Jingyi Xu,Fangyuan Gao,Xiancheng Sun,Mai Xu
标识
DOI:10.1109/tpami.2023.3334624
摘要
For multi-modal image processing, network interpretability is essential due to the complicated dependency across modalities. Recently, a promising research direction for interpretable network is to incorporate dictionary learning into deep learning through unfolding strategy. However, the existing multi-modal dictionary learning models are both single-layer and single-scale, which restricts the representation ability. In this paper, we first introduce a multi-scale multi-modal convolutional dictionary learning ( M
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