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Artificial Intelligence & Machine Learning vocabulary · Fine-Tuning and Adaptation

Transfer learning

Meaning in artificial intelligence & machine learning, with examples

nounCommon term

In artificial intelligence & machine learning, transfer learning means: Reusing what a model learned on one task or dataset as the starting point for a different but related task, instead of training a new model from scratch.

In Turkish: transfer öğrenme, aktarım öğrenmesi

How it is used

The standard approach when data is limited: take a pretrained model, replace or add a few layers and fine-tune it on your own data. Fine-tuning is the most common form of transfer learning.

Example sentences

  1. Thanks to transfer learning, 2,000 labeled images were enough to reach 95% accuracy.

  2. We used transfer learning from a general image model to detect defects in steel.

Common collocations

  • use transfer learning
  • transfer learning approach
  • pretrained backbone
  • domain adaptation
  • fine-tuningExtra training of an already pretrained model on a smaller, task-specific dataset, so that it does better on that task or follows a particular style.
  • pretrainingThe first and largest training stage, in which a model learns general patterns from a huge amount of mostly unlabeled data before it is adapted to specific tasks.
  • base modelA model that has only been pretrained, before any fine-tuning or instruction training. It continues text well but does not reliably follow instructions or chat.
  • CNNShort for convolutional neural network: a network that slides small learned filters over an image to detect patterns such as edges, textures and shapes.

This word in other fields

The same English word can have a different meaning in another field:

More terms: Fine-Tuning and Adaptation

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