CS5720 - Domain Adaptation

What is Domain Adaptation?

Domain Adaptation is a machine learning technique that enables models 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: Rich labeled training data
• Target Domain: Limited/no labeled data
• Domain Shift: Distribution differences between domains
• Feature Alignment: Making domains more similar

🎯 The Challenge

Models often fail when deployed to new domains due to distribution shift. Domain adaptation bridges this gap by learning domain-invariant features.

Adaptation Approaches

⚔️

Adversarial Adaptation

Use adversarial training to learn domain-invariant features
🎨

Fine-tuning

Adapt pre-trained models to new domains with limited data
🔄

Feature Alignment

Align feature distributions across domains explicitly
🤖

Self-Supervised

Use unlabeled target data for domain-specific learning

Domain Adaptation Visualization

Source Domain

📊 Rich labeled data
🎯 High-quality annotations
📈 Good performance

→

Target Domain

❓ Limited/no labels
🔄 Different distribution
⚠️ Performance gap

→

Adapted Model

✅ Domain-invariant features
🎯 Improved target performance
🔗 Bridged domain gap

Goal: Transfer knowledge from data-rich source to data-scarce target domain

Domain Adaptation

What is Domain Adaptation?

Adaptation Approaches

Domain Adaptation Visualization

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