Domain Adaptation
term_id: domain_adaptation
Category: training_techniques
Definition
Domain adaptation addresses the challenge when training and testing data come from different distributions. By aligning feature representations between a labeled source domain and an unlabeled or sparsely labeled target domain, models can generalize better to new environments. This technique is crucial for deploying AI systems in real-world scenarios where data characteristics shift over time or vary across regions, ensuring robustness without requiring extensive new labeled datasets.
Summary
A machine learning method that improves model performance on a target domain by leveraging knowledge from a source domain.
Key Concepts
- Source Domain
- Target Domain
- Distribution Shift
- Feature Alignment
Use Cases
- Translating medical images across different scanner types
- Adapting speech recognition to different accents
- Cross-platform recommendation systems