Definition
Transfer learning applies knowledge from a pre-trained model to a new, related task.
Why It Works: - Models learn general features first - These features transfer to related tasks - Reduces need for task-specific data
Process: 1. Pre-train on large dataset (e.g., ImageNet, internet text) 2. Fine-tune on smaller, specific dataset 3. Achieve better results than training from scratch
Benefits: - Less training data needed - Faster training - Better performance - Lower compute costs
Examples: - ImageNet pre-trained models for medical imaging - GPT pre-training enabling diverse tasks - BERT fine-tuned for sentiment analysis
Examples
Using a model trained on general images to classify specific medical scans.
Related Terms
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