Transfer Learning

Overview

A machine learning technique where a model trained on one task is repurposed for a different but related task. It leverages the knowledge gained during the initial training phase to improve the model's performance and reduce training time on the new task, by transferring learned weights and representations from one task to another.

Reusing Trained Models

Transfer learning is when a model trained on one task is reused for a different, but related task.

Improving New Training

It uses initial training to improve and accelerate new training on a target task, and does not require starting from scratch.

Reduced Resources

It requires less data and less training time, thus saving valuable time and effort for developers by leveraging a pre-existing trained model.

Related Concepts

• Machine Learning
• Deep Learning
• Fine-tuning
• Pre-training
• Domain Adaptation