Semi-Supervised Learning

➺ Core Concepts:

  • Mixed data utilization
  • Pseudo-labeling
  • Co-training
  • Active learning 

    • ➺ Technical Approaches:

    Methods 

    • Self-training
    • Multi-view learning
    • Label propagation
    • Graph-based methods 

    ➺ Implementation Strategies:

  • Consistency regularization
  • Entropy minimization
  • Virtual adversarial training
  • MixMatch algorithms

    • ➺ Practical Applications:

    Use Cases 

    • Medical image analysis
    • Text classification
    • Speech recognition
    • Object detection 

    ➺ Industry Solutions:

  • Content categorization
  • Sentiment analysis
  • Document classification
  • Image segmentation