ML-Assisted Annotation System

An ML-Assisted Annotation System is an annotation system that leverages machine learning models to provide automated suggestions and pre-annotations during the data labeling process.

• AKA: Machine Learning Assisted Annotation System, AI-Powered Annotation System, Model-Assisted Labeling System, Intelligent Annotation System.
• Context:
  • It can typically use pre-trained models to generate initial annotations that human annotators can review and refine.
  • It can typically implement active learning to prioritize uncertain examples for human review.
  • It can typically support confidence scores for model predictions to guide annotation decisions.
  • It can often adapt to annotator feedback through online learning mechanisms.
  • It can often reduce annotation time by providing intelligent suggestions and auto-completion features.
  • It can often enable transfer learning from existing datasets to new annotation projects.
  • It can range from being a Rule-Based Assisted System to being a Deep Learning Assisted System, depending on its underlying technology.
  • It can range from being a Single-Model System to being a Multi-Model Ensemble System, depending on its model architecture.
  • It can range from being a Domain-Specific Assisted System to being a General-Purpose Assisted System, depending on its application scope.
  • It can range from being a Suggestion-Only System to being a Full Auto-Annotation System, depending on its automation level.
  • ...
• Example(s):
  • Text ML-Assisted Annotation Systems, such as:
    • Prodigy Tool with spaCy integration for NLP tasks.
    • Label Studio with ML backends for text classification.
  • Image ML-Assisted Annotation Systems, such as:
    • CVAT with automatic annotation for object detection.
    • Supervisely with neural networks for segmentation tasks.
  • Multi-Modal ML-Assisted Annotation Systems, such as:
    • Scale AI Platform with multiple models.
  • ...
• Counter-Example(s):
  • Manual Annotation System, which lacks ML assistance.
  • Rule-Based Annotation System, which uses static rules without learning.
  • Fully Automated Annotation System, which excludes human involvement.
• See: Label Studio, Active Learning System, Human-in-the-Loop System, Pre-Annotation Tool, Open Source Annotation Tool, Multi-Modal Annotation Framework, Annotation Quality Control System, Active Learning Annotation Strategy, Collaborative Annotation Platform.

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