Deep neural network models have recently draw lots of attention, as it consistently produce impressive results in many computer vision tasks such as image classification, object detection, etc. However, interpreting such model and show the reason why it performs quite well becomes a challenging question. In this paper, we propose a novel method to interpret the neural network models with attention mechanism. Inspired by the heatmap visualization, we analyze the relation between classification accuracy with the attention based heatmap. An improved attention based method is also included and illustrate that a better classifier can be interpreted by the attention based heatmap.
@article{arxiv.1901.10042,
title = {Deep Features Analysis with Attention Networks},
author = {Shipeng Xie and Da Chen and Rong Zhang and Hui Xue},
journal= {arXiv preprint arXiv:1901.10042},
year = {2019}
}
Comments
In AAAI-19 Workshop on Network Interpretability for Deep Learning