Explanation-Based Attention for Semi-Supervised Deep Active LearningDownload PDF

20 Mar 2019, 20:37 (modified: 12 Jul 2022, 20:41)LLD 2019Readers: Everyone
Keywords: active learning, attention, explanation, feature extraction
TL;DR: We introduce an attention mechanism to improve feature extraction for deep active learning (AL) in the semi-supervised setting.
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.
3 Replies

Loading