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Found 3 papers, 2 papers with code
One-shot Active Learning Based on Lewis Weight Sampling for Multiple Deep Models
Active learning (AL) for multiple target models aims to reduce labeled data querying while effectively training multiple models concurrently.
MUS-CDB: Mixed Uncertainty Sampling with Class Distribution Balancing for Active Annotation in Aerial Object Detection
However, existing active learning methods are mainly with class-balanced settings and image-based querying for generic object detection tasks, which are less applicable to aerial object detection scenarios due to the long-tailed class distribution and dense small objects in aerial scenes.
ALiPy: Active Learning in Python
Supervised machine learning methods usually require a large set of labeled examples for model training.
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