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

arXiv: https://arxiv.org/abs/2306.02257 · HTML: https://mlsanity.com/paper/2306.02257/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 12 references that are in the map.
- First posted on arXiv 4 Jun 2023.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [Network In Network](https://mlsanity.com/paper/1312.4400/) (2013)
- [Attention Branch Network: Learning of Attention Mechanism for Visual Explanation](https://mlsanity.com/paper/1812.10025/) (2018)
- [Embedding Human Knowledge into Deep Neural Network via Attention Map](https://mlsanity.com/paper/1905.03540/) (2019)

## Abstract (arXiv)

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.