Transfer Learning

Transfer learning is a machine learning strategy that takes a model developed for one task and repurposes it for a second, related task. The core insight is that features learned from large datasets on general tasks (such as ImageNet for vision or large text corpora for NLP) are broadly useful and can be transferred to more specific problems where labeled data may be scarce.

In practice, transfer learning typically involves taking a pretrained model, freezing some or all of its early layers (which capture general features), and training the later layers on the new task's dataset. This dramatically reduces training time and data requirements compared to training from scratch. The pretrained model serves as a strong initialization that already understands fundamental patterns in the data domain.

Transfer learning has been a driving force behind the democratization of AI. Before its widespread adoption, achieving state-of-the-art results required massive datasets and computational resources. Now, practitioners can fine-tune openly available pretrained models to achieve strong results on specialized tasks with limited data, making advanced AI accessible to smaller teams and organizations.