Research map / Label-efficient and robust learning / Recognition and long-tail
Provable Selective Auto-labeling with Reliability Guarantees
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Proposes a selective auto-labeling method with provable reliability guarantees on the trustworthiness of machine-labeled examples, rather than relying on heuristic model-confidence thresholds.
Problem
Auto-labeling performance is inconsistent across tasks, and existing selective auto-labeling heuristics depend on model confidence scores and give no reliable guarantee on the trustworthiness of the examples they select.
Approach
Replaces heuristic confidence-based selection with a procedure that provides provable (statistical) reliability guarantees on the accuracy of the automatically labeled subset.
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