Hierarchical Subquery Evaluation for Active Learning on a Graph
To train good supervised and semi-supervised object classifiers, it is critical that we not waste the time of the human experts who are providing the training labels. Existing active learning strategies can have uneven performance, being efficient on some datasets but wasteful on others, or inconsistent just between runs on the same dataset. We propose perplexity-based graph construction and a new hierarchical subquery evaluation algorithm to combat this variability, and to release the potential of Expected Error Reduction.
Version 1.0 can be downloaded here [code].
Contact: omacaodh (@) cs.ucl.ac.uk