Model Transfer Method

A Model Transfer Method is a machine learning method that adapts knowledge from source models or domains to improve performance on target tasks or domains.

• AKA: Transfer Method, Knowledge Transfer Method, Domain Transfer Method.
• Context:
  • It can typically reuse Source Model Knowledge through model transfer mechanisms.
  • It can typically accelerate Target Task Learning via model transfer initializations.
  • It can often mitigate Data Scarcity through model transfer knowledge leverages.
  • It can often reduce Training Cost via model transfer computation reuses.
  • It can range from being a Feature Transfer Method to being a Parameter Transfer Method, depending on its model transfer knowledge type.
  • It can range from being a Homogeneous Transfer Method to being a Heterogeneous Transfer Method, depending on its model transfer domain similarity.
  • It can range from being a Zero-Shot Transfer Method to being a Few-Shot Transfer Method, depending on its model transfer target data requirement.
  • It can range from being a Static Transfer Method to being a Adaptive Transfer Method, depending on its model transfer adjustment capability.
  • ...
• Examples:
  • Pre-Training Transfer Methods, such as:
    • Language Model Pre-Training for contextual representation transfer.
    • Vision Model Pre-Training for visual feature transfer.
    • Multi-Modal Pre-Training for cross-modal transfer.
  • Fine-Tuning Transfer Methods, such as:
    • Full Fine-Tuning Method for complete adaptation.
    • Layer-Wise Fine-Tuning for selective adjustment.
    • Parameter-Efficient Fine-Tuning for resource-constrained adaptation.
  • Domain Adaptation Methods, such as:
    • Adversarial Domain Adaptation for distribution alignment.
    • Self-Training Domain Adaptation for pseudo-label utilization.
    • Feature Alignment Method for representation matching.
  • Knowledge Distillation Methods, such as:
    • Response-Based Distillation for output matching.
    • Feature-Based Distillation for intermediate transfer.
    • Relation-Based Distillation for structural knowledge transfer.
  • ...
• Counter-Examples:
  • Scratch Training Method, which learns without prior knowledge.
  • Multi-Task Learning Method, which learns tasks jointly rather than transferring.
  • Ensemble Method, which combines models rather than transferring knowledge.
• See: Machine Learning Method, Transfer Learning Task, Domain Adaptation, Knowledge Transfer, Pre-Trained Model, Fine-Tuning Process, Model Adaptation.

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