Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer
Abstract
Attention plays a critical role in human visual experience. Furthermore, it has recently been demonstrated that attention can also play an important role in the context of applying artificial neural networks to a variety of tasks from fields such as computer vision and NLP. In this work we show that, by properly defining attention for convolutional neural networks, we can actually use this type of information in order to significantly improve the performance of a student CNN network by forcing it to mimic the attention maps of a powerful teacher network. To that end, we propose several novel methods of transferring attention, showing consistent improvement across a variety of datasets and convolutional neural network architectures. Code and models for our experiments are available at this https URL
Cite
Text
Zagoruyko and Komodakis. "Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer." International Conference on Learning Representations, 2017.Markdown
[Zagoruyko and Komodakis. "Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer." International Conference on Learning Representations, 2017.](https://mlanthology.org/iclr/2017/zagoruyko2017iclr-paying/)BibTeX
@inproceedings{zagoruyko2017iclr-paying,
title = {{Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer}},
author = {Zagoruyko, Sergey and Komodakis, Nikos},
booktitle = {International Conference on Learning Representations},
year = {2017},
url = {https://mlanthology.org/iclr/2017/zagoruyko2017iclr-paying/}
}