Transfer Learning

A machine learning technique where a model trained on a large general dataset is adapted to perform a new, more specific task using a much smaller amount of new training data. Rather than training a model from scratch, transfer learning leverages patterns already learned by an existing model (such as a neural network pre-trained on millions of images) and fine-tunes it for a specific purpose. This approach is particularly valuable in accessibility applications because it allows end users to create effective personalized models — such as custom object recognizers — from just a few dozen example images, making AI tools practical for individual customization.