Explanation-Based Attention for Semi-Supervised Deep Active Learning

Mar 20, 2019 Blind Submission readers: 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.
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