Training Strategy

A Training Strategy is a learning strategy that defines the approach and methodology for training machine learning models.

• AKA: Learning Strategy, Training Approach, Training Methodology.
• Context:
  • It can typically optimize Model Learning through training objective designs.
  • It can typically manage Training Resources via training schedule plannings.
  • It can often improve Training Efficiency through training sample selections.
  • It can often enhance Model Generalization via training regularization techniques.
  • It can range from being a Supervised Training Strategy to being a Unsupervised Training Strategy, depending on its training label availability.
  • It can range from being a Batch Training Strategy to being a Online Training Strategy, depending on its training data processing mode.
  • It can range from being a Single-Stage Training Strategy to being a Multi-Stage Training Strategy, depending on its training phase count.
  • It can range from being a Standard Training Strategy to being a Adaptive Training Strategy, depending on its training parameter adjustment.
  • ...
• Examples:
  • Supervised Training Strategys, such as:
    • Gradient Descent Training Strategy for parameter optimization.
    • Stochastic Training Strategy for large-scale learning.
    • Mini-Batch Training Strategy for efficiency balance.
  • Semi-Supervised Training Strategys, such as:
    • Self-Training Strategy for pseudo-label generation.
    • Co-Training Strategy for multi-view learning.
    • Consistency Training Strategy for regularization.
  • Transfer Training Strategys, such as:
    • Fine-Tuning Strategy for domain adaptation.
    • Feature Extraction Strategy for representation transfer.
    • Progressive Training Strategy for incremental learning.
  • Specialized Training Strategys, such as:
    • Adversarial Training Strategy for robustness improvement.
    • Curriculum Training Strategy for complexity progression.
    • Meta-Learning Strategy for learning to learn.
  • ...
• Counter-Examples:
  • Model Architecture, which defines structure rather than training approach.
  • Inference Strategy, which concerns deployment rather than training.
  • Data Collection Strategy, which focuses on data gathering rather than model training.
• See: Learning Strategy, Machine Learning Task, Training Algorithm, Optimization Method, Model Training Process, Learning Theory, Training Hyperparameter.

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