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Knowledge Transfer

Knowledge transfer refers to the process of leveraging learned information from one context (model, task, domain) to improve learning or performance in another context. This is a fundamental concept in machine learning that enables efficiency gains and generalization.

Forms of knowledge transfer

Parameter Transfer: Reusing weights and biases from a pretrained model as initialization or feature extractors for new tasks.

Representation Transfer: Leveraging learned feature representations without necessarily reusing parameters directly.

Knowledge Consolidation: Combining or merging knowledge from multiple models into a unified representation.

Feature Extraction: Using learned features from one domain to bootstrap learning in another.

Mechanisms

Transfer learning is realized through fine-tuning, feature extraction, and increasingly through model merging and ensemble techniques that preserve knowledge across different training objectives.

Key papers

  • [[2023-dahein-model-merging]] — uncertainty-based model merging preserves knowledge from multiple task-specific models