Translations:Transfer Learning/24/en: Difference between revisions

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    * '''Data augmentation''' complements transfer learning by artificially expanding the effective size of the target dataset.
    * '''Data augmentation''' complements transfer learning by artificially expanding the effective size of the target dataset.
    * '''{{Term|learning rate}} warmup''' helps stabilise early training when {{Term|fine-tuning}} large pretrained models.
    * '''Learning rate warmup''' helps stabilise early training when fine-tuning large pretrained models.
    * '''Early stopping''' on a validation set prevents {{Term|overfitting}} during {{Term|fine-tuning}}, especially with small datasets.
    * '''Early stopping''' on a validation set prevents overfitting during fine-tuning, especially with small datasets.
    * '''Layer-wise {{Term|learning rate}} decay''' assigns smaller rates to earlier (more general) layers and larger rates to later (more task-specific) layers.
    * '''Layer-wise learning rate decay''' assigns smaller rates to earlier (more general) layers and larger rates to later (more task-specific) layers.
    * '''Intermediate task transfer''' — {{Term|fine-tuning}} on a related intermediate task before the final target (e.g., NLI before sentiment analysis) can further improve results.
    * '''Intermediate task transfer''' — fine-tuning on a related intermediate task before the final target (e.g., NLI before sentiment analysis) can further improve results.

    Revision as of 22:05, 27 April 2026

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    Message definition (Transfer Learning)
    * '''Data augmentation''' complements transfer learning by artificially expanding the effective size of the target dataset.
    * '''{{Term|learning rate}} warmup''' helps stabilise early training when {{Term|fine-tuning}} large pretrained models.
    * '''Early stopping''' on a validation set prevents {{Term|overfitting}} during {{Term|fine-tuning}}, especially with small datasets.
    * '''Layer-wise {{Term|learning rate}} decay''' assigns smaller rates to earlier (more general) layers and larger rates to later (more task-specific) layers.
    * '''Intermediate task transfer''' — {{Term|fine-tuning}} on a related intermediate task before the final target (e.g., NLI before sentiment analysis) can further improve results.
    • Data augmentation complements transfer learning by artificially expanding the effective size of the target dataset.
    • Learning rate warmup helps stabilise early training when fine-tuning large pretrained models.
    • Early stopping on a validation set prevents overfitting during fine-tuning, especially with small datasets.
    • Layer-wise learning rate decay assigns smaller rates to earlier (more general) layers and larger rates to later (more task-specific) layers.
    • Intermediate task transfer — fine-tuning on a related intermediate task before the final target (e.g., NLI before sentiment analysis) can further improve results.
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