AI Terms

Transfer Learning

Definition: Transfer learning is a machine learning technique that involves training a model on one task and then adapting it to perform a related task.

Detailed Description:

Transfer learning can be useful when there is limited data available for the target task, or when the target task is similar to a task that the model has already been trained on. By transferring knowledge from a pre-trained model, you can often achieve better performance with less training data.

Common transfer learning techniques include:

  • Fine-tuning: Adapting a pre-trained model to a new task by training it on a smaller dataset.
  • Feature extraction: Using the features learned by a pre-trained model as input for a new task.
  • Domain adaptation: Adapting a model to a new domain with different data distribution.

Transfer learning is widely used in various applications, such as:

  • Natural language processing: Adapting pre-trained language models to specific tasks like text classification or question answering.
  • Computer vision: Fine-tuning pre-trained image models for tasks like object detection or image segmentation.
  • Speech recognition: Adapting pre-trained speech models to different accents or languages.

Related Terms:

FAQs:

  1. What is the difference between transfer learning and training a model from scratch?

    • Transfer learning starts with a pre-trained model, while training from scratch involves training a model from the beginning on a large dataset.
  2. When is transfer learning useful?

    • Transfer learning is useful when there is limited data available for the target task or when the target task is similar to a task that the pre-trained model was originally trained on.
  3. What are some challenges of transfer learning?

    • Challenges include finding a suitable pre-trained model, adapting the model to the new task, and avoiding overfitting.

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