Abstract
For instance-level explanation, in order to reveal the relations between high-level semantics and detailed spatial information, this paper proposes a novel cognitive approach to neural networks, which named PANE. Under the guidance of PANE, a novel saliency map representation method, named IOM, is proposed for CNN-like models. We make the comparison with eight state-of-the-art saliency map representation methods. The experimental results show that IOM far outperforms baselines. The work of this paper may bring a new perspective to understand deep neural networks.
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URL
https://arxiv.org/abs/2205.00932
