Transfer Learning

Transfer learning is a machine learning technique where a model developed for one task is reused as the starting point for a model on a second task. This approach is particularly useful when the second task has limited data available.

How Transfer Learning Works

In transfer learning, a pre-trained model on a large dataset (like ImageNet for image tasks) is adapted for a new, related task. For instance, the feature extraction layers of a pre-trained image classification model might be reused for a new image recognition task, with only the final classification layers being trained from scratch.