EXPONA improves automated data labeling by exploring multi-level label functions and applying reliability filters, achieving up to 98.9% coverage and 46% gains in downstream weighted F1 on eleven datasets.
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Adaptive confidence threshold selection improves F1 scores in explainable multi-task classification for autonomous driving and is supported by a new 958-image dataset.
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Structured Exploration and Exploitation of Label Functions for Automated Data Annotation
EXPONA improves automated data labeling by exploring multi-level label functions and applying reliability filters, achieving up to 98.9% coverage and 46% gains in downstream weighted F1 on eleven datasets.
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Beyond Fixed Thresholds and Domain-Specific Benchmarks for Explainable Multi-Task Classification in Autonomous Vehicles
Adaptive confidence threshold selection improves F1 scores in explainable multi-task classification for autonomous driving and is supported by a new 958-image dataset.