In this work, we aim to realize a method for embedding human knowledge into
deep neural networks. While the conventional method to embed human knowledge
has been applied for non-deep machine learning, it is challenging to apply it
for deep learning models due to the enormous number of model parameters. To
tackle this problem, we focus on the attention mechanism of an attention branch
network (ABN). In this paper, we propose a fine-tuning method that utilizes a
single-channel attention map which is manually edited by a human expert. Our
fine-tuning method can train a network so that the output attention map
corresponds to the edited ones. As a result, the fine-tuned network can output
an attention map that takes into account human knowledge. Experimental results
with ImageNet, CUB-200-2010, and IDRiD demonstrate that it is possible to
obtain a clear attention map for a visual explanation and improve the
classification performance. Our findings can be a novel framework for
optimizing networks through human intuitive editing via a visual interface and
suggest new possibilities for human-machine cooperation in addition to the
improvement of visual explanations.

本研究旨在实现一种将人类知识嵌入深度神经网络的方法，通过人类知识的可视化编辑，利用注意力机制进行微调，以实现从视觉角度获得更人类可解释的分类结果。