Transfer Learning

Transfer learning is a machine learning approach where knowledge gained from training on one task is applied to a different but related task, significantly reducing the data and compute required to achieve good performance. A model trained on ImageNet's millions of labeled images learns general visual features, edges, textures, shapes, that transfer to medical imaging, satellite analysis, or manufacturing inspection. Fine-tuning this pretrained model on a few hundred domain-specific examples often outperforms training a new model from scratch on thousands. The pretrained features provide a powerful starting point; fine-tuning adapts them to the specific task. Transfer learning works because many tasks share underlying structure. Low-level features like edge detectors in vision or syntactic patterns in language are universally useful. High-level features may need more adaptation. The closer the source and target domains, the better transfer learning works. This approach transformed AI development economics. Instead of every organization training models from scratch, they start from publicly available pretrained models. Foundation models like GPT and BERT are designed specifically to transfer, they learn broad representations from massive pretraining that adapt to diverse downstream tasks with minimal fine-tuning. Transfer learning is why modern AI is accessible: most practitioners fine-tune rather than pretrain, building on billions of dollars of compute invested by AI labs.