MACHINE PERCEPTION AND ROBOTICS GROUP

Dept. of Robotics Science and Technology,
Chubu University

The conventional method to embed human knowledge has been applied for non-deep machine learning. Meanwhile, it is challenging to apply it for deep learning models due to the enormous number of model parameters. In this paper, we propose a novel framework for optimizing networks while embedding human knowledge. The crucial factors are an attention map for visual explanation and an attention mechanism. A manually edited attention map, in which human knowledge is embedded, has the potential to adjust recognition results. The proposed method updates network parameters so that the output attention map corresponds to the edited ones. As a result, the trained 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.