Transfer learning is a fundamental concept in the field of deep learning and artificial intelligence.
“It refers to the practice of leveraging knowledge gained from one machine learning task and applying it to another related or even unrelated task.”
This approach allows models to transfer their learned representations or features, often trained on vast datasets, to new problems, saving both time and computational resources.
Transfer Learning Strategies
- Parameter transfer: used when the models for related tasks share some parameters, including the application of additional weightage to target the domain to improve performance.
- Feature-representation transfer: identifies good feature representations from source up to target domains to minimize domain divergence.
- Relational-knowledge transfer: attempts to handle non-IID data through relational-knowledge-transfer techniques.
There are various techniques within transfer learning, including fine-tuning pre-trained models, feature extraction, and domain adaptation, each tailored to different scenarios.
Transfer learning isn’t limited to image recognition; it’s applicable across various domains, including natural language processing and speech recognition.