Transfer Learning
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.