Active Learning with Partial Feedback
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In the large-scale multiclass setting, assigning labels often consists of answering multiple questions to drill down through a hierarchy of classes. Here, the labor required per annotation scales with the number of questions asked. We propose active learning with partial feedback. In this setup, the learner asks the annotator if a chosen example belongs to a (possibly composite) chosen class. The answer eliminates some classes, leaving the agent with a partial label. Success requires (i) a sampling strategy to choose (example, class) pairs, and (ii) learning from partial labels. Experiments on the TinyImageNet dataset demonstrate that our most effective method achieves a 21 Experiments on the TinyImageNet dataset demonstrate that our most effective method achieves a 26 classification accuracy for a 250k (or 30 a naive baseline. Our work may also impact traditional data annotation. For example, our best method fully annotates TinyImageNet with only 482k (with EDC though, ERC is 491) binary questions (vs 827k for naive method).
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