Transfer Learning
Applying knowledge learned from one task to a different but related task. This allows models to achieve good performance with less training data and compute.
In-Depth Explanation
Transfer learning leverages knowledge from one domain or task to improve performance on another. It's fundamental to modern AI, enabling powerful capabilities without training from scratch.
How transfer learning works:
- Pre-training: Model learns general patterns from large datasets
- Transfer: These learned representations apply to new tasks
- Fine-tuning: Optionally adjust the model for specific needs
Types of transfer learning:
- Feature extraction: Use pre-trained model as fixed feature generator
- Fine-tuning: Update model weights for new task
- Domain adaptation: Adapt from source to target domain
- Zero/few-shot: Apply without or with minimal task-specific data
Why transfer learning matters:
- Reduces data requirements dramatically
- Cuts training time and compute costs
- Enables AI for smaller organisations
- Makes rare tasks feasible
- Foundation for LLM capabilities
Business Context
Transfer learning democratises AI - businesses don't need millions of examples or massive compute budgets. Pre-trained models bring general intelligence; you add domain specifics.
How Clever Ops Uses This
We leverage transfer learning to deliver AI solutions quickly for Australian businesses. Starting from pre-trained models means faster deployment and lower data requirements.
Example Use Case
"Fine-tuning a pre-trained language model on 500 examples of your company's customer emails to classify inquiries accurately, instead of needing 50,000 examples."