Translations:Transfer Learning/11/en: Difference between revisions

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    | '''Feature extraction''' || Freeze all pretrained layers; train only a new output head || Very small target dataset; source and target are closely related
    | '''Feature extraction''' || Freeze all pretrained layers; train only a new output head || Very small target dataset; source and target are closely related
    |-
    |-
    | '''{{Term|fine-tuning}} (full)''' || Unfreeze all layers and train end-to-end with a small {{Term|learning rate}} || Moderate target dataset; source and target differ meaningfully
    | '''Fine-tuning (full)''' || Unfreeze all layers and train end-to-end with a small learning rate || Moderate target dataset; source and target differ meaningfully
    |-
    |-
    | '''Gradual unfreezing''' || Progressively unfreeze layers from top to bottom over training || Balances stability of lower features with adaptation of higher ones
    | '''Gradual unfreezing''' || Progressively unfreeze layers from top to bottom over training || Balances stability of lower features with adaptation of higher ones
    |}
    |}

    Revision as of 22:05, 27 April 2026

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    Message definition (Transfer Learning)
    {| class="wikitable"
    |-
    ! Strategy !! Description !! When to use
    |-
    | '''Feature extraction''' || Freeze all pretrained layers; train only a new output head || Very small target dataset; source and target are closely related
    |-
    | '''{{Term|fine-tuning}} (full)''' || Unfreeze all layers and train end-to-end with a small {{Term|learning rate}} || Moderate target dataset; source and target differ meaningfully
    |-
    | '''Gradual unfreezing''' || Progressively unfreeze layers from top to bottom over training || Balances stability of lower features with adaptation of higher ones
    |}
    Strategy Description When to use
    Feature extraction Freeze all pretrained layers; train only a new output head Very small target dataset; source and target are closely related
    Fine-tuning (full) Unfreeze all layers and train end-to-end with a small learning rate Moderate target dataset; source and target differ meaningfully
    Gradual unfreezing Progressively unfreeze layers from top to bottom over training Balances stability of lower features with adaptation of higher ones
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