Transfer Learning
Transfer Learning is a machine learning technique where a model developed for one task is reused as the starting point for a model on a second, related task, significantly reducing the amount of labeled data and compute needed.
Frequently Asked Questions
What is the primary advantage of Transfer Learning?▼
It allows developers to train highly accurate models on very small datasets because the model starts with pre-learned features (like edge detection in images or grammar/world knowledge in text).
What is negative transfer in Transfer Learning?▼
Negative transfer occurs when the knowledge learned from the source task impairs performance on the target task, usually because the two tasks are too dissimilar.
Quick Facts
- CategoryModel Training
- Key ApplicationFine-tuning foundational models (like BERT or GPT) on domain-specific datasets, and applying pre-trained ImageNet models to medical image analysis.
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