Transfer Learning is a machine learning technique where a model trained on one task is reused or adapted as the starting point for a model on a related task. It leverages knowledge gained from one domain to improve learning and performance in another domain.
- Glossary > Letter: T
What does "Transfer Learning" mean?
Use Cases
Image Recognition:
Fine-tuning pre-trained models for specific image classification tasks.
Natural Language Processing:
Using pre-trained language models for various downstream tasks like sentiment analysis or named entity recognition.
Medical Imaging:
Transferring knowledge from large datasets to improve diagnostic accuracy on smaller datasets.
Importance
Efficiency:
Reduces the need for large annotated datasets and computational resources.
Adaptability:
Facilitates rapid development and deployment of models in new domains.
Performance Boost:
Enhances model performance by leveraging learned features and representations from related tasks.
Analogies
Transfer Learning is like applying knowledge from learning to ride a bicycle to learning to ride a motorcycle. Just as balancing skills learned from riding a bicycle can be transferred to riding a motorcycle, transfer learning applies knowledge from one task to improve performance on another task.
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