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:

• Machine Learning
• Pre-trained Models
• Fine-Tuning
• Domain Adaptation
• Feature Extraction

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.