Active Learning Annotation Strategy

An Active Learning Annotation Strategy is an annotation strategy that uses machine learning algorithms to identify and prioritize the most informative examples for human annotation to maximize model performance with minimal labeling effort.

• AKA: Active Learning Labeling Strategy, Intelligent Sampling Strategy, Query-Based Annotation Strategy, Selective Annotation Approach.
• Context:
  • It can typically employ uncertainty sampling to select ambiguous examples near decision boundaries.
  • It can typically use diversity sampling to ensure representative coverage of the data distribution.
  • It can often implement query-by-committee where multiple models vote on uncertain instances.
  • It can often reduce annotation costs by focusing on high-value examples.
  • It can typically iterate between model training and sample selection phases.
  • It can often provide performance estimates to determine stopping criteria.
  • It can range from being a Pool-Based Active Learning to being a Stream-Based Active Learning, depending on its data access pattern.
  • It can range from being a Single-Criterion Strategy to being a Multi-Criterion Strategy, depending on its selection metrics.
  • It can range from being a Myopic Strategy to being a Batch-Aware Strategy, depending on its planning horizon.
  • It can range from being a Model-Agnostic Strategy to being a Model-Specific Strategy, depending on its algorithm dependency.
  • ...
• Example(s):
  • Uncertainty-Based Active Learning Annotation Strategys, such as:
    • Least Confidence Sampling in Prodigy Tool.
    • Entropy-Based Selection in modAL Framework.
  • Diversity-Based Active Learning Annotation Strategys, such as:
    • Core-Set Selection for representative sampling.
    • Cluster-Based Sampling in Label Studio.
  • Hybrid Active Learning Annotation Strategys, such as:
    • Uncertainty-Diversity Tradeoff in ALiPy Library.
  • ...
• Counter-Example(s):
  • Random Sampling Strategy, which lacks intelligent selection.
  • Exhaustive Annotation Strategy, which labels all examples.
  • Passive Learning Strategy, which uses predetermined datasets.
• See: Label Studio, Active Learning Task, ML-Assisted Annotation System, Annotation Quality Control System, Open Source Annotation Tool, Query Strategy, Sample Selection Algorithm, Human-in-the-Loop Learning, Annotation Efficiency.

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