Domain Adaptation is the process of adapting a model trained on one domain (source) to perform well on a different but related domain (target) where labeled data may be scarce or unavailable.
Key Concepts:
• Source Domain - Where we have labeled training data
• Target Domain - Where we want to apply the model
• Domain Shift - Differences between domains
• Distribution Gap - Statistical differences in data
⚠️ The Domain Gap Challenge
Models trained on one domain often perform poorly on another due to differences in data distribution, even when the tasks are similar.
Adaptation Methods
📊
Supervised Adaptation
Using labeled data from target domain
🔄
Unsupervised Adaptation
Adapting without target domain labels
⚔️
Adversarial Adaptation
Using adversarial training for domain alignment
Success Factors:
Domain similarity, data quality, and appropriate adaptation technique selection are crucial for success.
Domain Adaptation Process
Source Domain
Natural Images
(ImageNet) Rich Labels ✓
❌
Target Domain
Medical Images
(X-rays) Limited Labels ⚠️
→
Adapted Model
Bridged Domains
(Aligned Features) Good Performance ✓
Click on each domain to explore the adaptation challenges and solutions