Home/Agentic AI/Cross-Domain Agents/Transfer Mechanisms

Cross-Domain Agents

Design agents that transfer knowledge across different domains

Your Progress

0 / 5 completed
1
2
3
4
5

How Knowledge Transfers

There are four primary mechanisms for transferring knowledge from a source domain to a target domain. Each has different trade-offs in difficulty, effectiveness, and data requirements.

Interactive: Transfer Mechanism Explorer

Explore how each transfer mechanism works:

🔧
Feature Transfer
Reuse learned feature representations
Low
Effectiveness
85%
How It Works:
Early layers of neural networks learn general features (edges, textures, shapes) that work across domains. Freeze these layers, retrain only final layers for new domain.
Example:
Transfer visual features from ImageNet to medical imaging
Best For:
Same modality (image→image, text→text), similar task types

Interactive: Transfer Impact Calculator

Adjust transfer strength to see impact on performance, time, and cost:

Transfer Strength
70%
No TransferPerfect Transfer
Target Accuracy
83%
vs 65% baseline
Training Time
75d
saved 105 days
Total Cost
$74K
saved $126K
🎯 Strong transfer - excellent knowledge reuse!
💡
Choosing the Right Mechanism
  • •Feature Transfer: Use when domains share visual/linguistic patterns (e.g., different image types)
  • •Architecture Transfer: Use when proven architecture exists but domains differ significantly
  • •Parameter Transfer: Use when source and target are related with 1K+ target examples
  • •Knowledge Distillation: Use when you need smaller, faster model for edge deployment
← Introduction