标题： 从人工智能学习：一种使用包含专家知识作为教师的DNN模型的交互式学习方法 显示英文标题

标题： Learning from AI: An Interactive Learning Method Using a DNN Model Incorporating Expert Knowledge as a Teacher

摘要： 视觉解释是一种通过深度学习可视化判断依据的方法，可以通过可视化注意力图来直观解释特定输入的判断依据。 对于输出错误决策依据的深度学习模型，提出了一种方法，该方法通过注意力图将专家的人类知识纳入模型中，以提高解释能力和识别准确率。 在本研究中，基于融入专家知识的深度学习模型，提出了一种学习者“从AI学习”其决策依据的方法。 准备了一个经过专家修改的注意力图微调的“注意力分支网络”(ABN)作为教师。 通过使用针对微调后的ABN和注意力图的交互式编辑工具，学习者通过编辑注意力图并改变推理结果来进行学习。 通过反复编辑注意力图并进行推理，使正确的识别结果输出，学习者可以获取嵌入在ABN中的专家判断依据。 对受试者的评估实验结果表明，使用所提出的方法进行学习比传统方法更高效。 显示英文摘要

摘要： 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.