  Visual explanation is an approach for visualizing the grounds of judgment by
deep learning, and it is possible to visually interpret the grounds of a
judgment for a certain input by visualizing an attention map. As for
deep-learning models that output erroneous decision-making grounds, a method
that incorporates expert human knowledge in the model via an attention map in a
manner that improves explanatory power and recognition accuracy is proposed. In
this study, based on a deep-learning model that incorporates the knowledge of
experts, a method by which a learner "learns from AI" the grounds for its
decisions is proposed. An "attention branch network" (ABN), which has been
fine-tuned with attention maps modified by experts, is prepared as a teacher.
By using an interactive editing tool for the fine-tuned ABN and attention maps,
the learner learns by editing the attention maps and changing the inference
results. By repeatedly editing the attention maps and making inferences so that
the correct recognition results are output, the learner can acquire the grounds
for the expert's judgments embedded in the ABN. The results of an evaluation
experiment with subjects show that learning using the proposed method is more
efficient than the conventional method.
