Model Combination Pattern

A Model Combination Pattern is a machine learning pattern that combines two or more models to support adjacent tasks.

• AKA: Model Integration Pattern, Multi-Model Pattern.
• Context:
  • It can typically enable Model Knowledge Transfer with model parameter sharings.
  • It can typically support Adjacent Task Support through model output coordinations.
  • It can often improve Model Performance via model complementaritys.
  • It can often reduce Model Training Cost through model component reuses.
  • It can range from being a Simple Model Combination Pattern to being a Complex Model Combination Pattern, depending on its model interaction complexity.
  • It can range from being a Sequential Model Combination Pattern to being a Parallel Model Combination Pattern, depending on its model execution order.
  • It can range from being a Homogeneous Model Combination Pattern to being a Heterogeneous Model Combination Pattern, depending on its model architecture diversity.
  • It can range from being a Static Model Combination Pattern to being a Dynamic Model Combination Pattern, depending on its model selection strategy.
  • ...
• Examples:
  • Shared Architecture Model Combination Patterns, such as:
    • Multi-Task Model Combination Pattern for multi-task simultaneous learning.
    • Shared Encoder Model Combination Pattern for representation sharing.
  • Transfer-Based Model Combination Patterns, such as:
    • Transfer Domain Adaptation Model Combination Pattern for domain knowledge transfer.
    • Cross-Task Knowledge Distillation Model Combination Pattern for knowledge compression.
  • Iterative Model Combination Patterns, such as:
    • Co-Training Model Combination Pattern for semi-supervised learning.
    • Self-Training Model Combination Pattern for pseudo-label generation.
  • Pipeline Model Combination Patterns, such as:
    • Modular Pipeline Model Combination Pattern for sequential processing.
    • Retrieval-Augmented Generation Pattern for information retrieval integration.
  • ...
• Counter-Examples:
  • Single Model Architecture, which uses only one model without combination.
  • Model Ensemble Method, which combines predictions rather than model components.
  • Hyperparameter Optimization, which tunes single models rather than combining them.
• See: Machine Learning Pattern, Multi-Task Learning Task, Transfer Learning Task, Model Architecture, Neural Network Architecture, Deep Learning System, Model Training Strategy.

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