Transfer Learning: Stand on a Pretrained Model

Short summary

Transfer learning lets you stand on a pretrained model and reach high accuracy with limited data. The post compares accuracy curves (from-scratch vs transfer-based), explains feature extraction (freeze backbone, train new head) and fine-tuning (unfreeze top layers at low learning rate), and provides a recipe. This is why fine-tuning foundation models works: the model already learned language; you adapt it cheaply.

• •Feature extraction: freeze the pretrained backbone, replace the final classifier, and train only the new head
• •Fine-tuning: unfreeze top layers and train at low learning rate to adapt without breaking learned features
• •Transfer learning makes deep learning practical for those without massive labeled datasets or GPU farms

Generated with AI, which can make mistakes.