Explanation-Based Attention for Semi-Supervised Deep Active Learning
Abstract
We introduce an attention mechanism to improve feature extraction for deep active learning (AL) in the semi-supervised setting. The proposed attention mechanism is based on recent methods to visually explain predictions made by DNNs. We apply the proposed explanation-based attention to MNIST and SVHN classification. The conducted experiments show accuracy improvements for the original and class-imbalanced datasets with the same number of training examples and faster long-tail convergence compared to uncertainty-based methods.
Cite
Text
Gudovskiy et al. "Explanation-Based Attention for Semi-Supervised Deep Active Learning." ICLR 2019 Workshops: LLD, 2019.Markdown
[Gudovskiy et al. "Explanation-Based Attention for Semi-Supervised Deep Active Learning." ICLR 2019 Workshops: LLD, 2019.](https://mlanthology.org/iclrw/2019/gudovskiy2019iclrw-explanationbased/)BibTeX
@inproceedings{gudovskiy2019iclrw-explanationbased,
title = {{Explanation-Based Attention for Semi-Supervised Deep Active Learning}},
author = {Gudovskiy, Denis and Hodgkinson, Alec and Yamaguchi, Takuya and Tsukizawa, Sotaro},
booktitle = {ICLR 2019 Workshops: LLD},
year = {2019},
url = {https://mlanthology.org/iclrw/2019/gudovskiy2019iclrw-explanationbased/}
}