What is Transfer Learning?
Transfer Learning is a machine learning technique where a model developed for one task is adapted to perform a related task, reducing the need for large datasets. In Edge AI, it enables devices to leverage pretrained models for faster, efficient on-device intelligence. Synonym: knowledge transfer.
Why Is It Used?
- Reduces training time and computational costs.
- Minimizes data requirements for new tasks.
- Enhances AI performance on resource-constrained edge devices.
How Is It Used?
- Deploy a pretrained neural network on edge devices.
- Fine-tune the model using task-specific or local data.
- Integrate with Edge AI platforms for real-time inference and automation.
Types of Transfer Learning
- Inductive Transfer Learning: Applies knowledge from one domain to a different but related task.
- Transductive Transfer Learning: Uses source domain knowledge to improve performance on a similar target domain.
- Unsupervised Transfer Learning: Transfers knowledge where labeled data is scarce on the target task.
Benefits of Transfer Learning
- Faster deployment of AI solutions at the edge.
- Reduced dependency on large, labeled datasets.
- Optimized model performance for IoT and edge devices.
- Supports real-time decision-making without cloud reliance.