Knowledge hand-me-down, but cuter.
Transfer learning reuses a model trained on one task to jump-start learning on a different but related task.
Imagine you already know how to ride a bicycle, and now you want to learn to ride a motorcycle. You don't start from scratch—you already understand balance, steering, and pedaling. Transfer learning is like that: you take knowledge from one skill and apply it to a new, similar skill to learn faster.
It saves massive amounts of time and data, because you don't need to train a model from zero every time. You see it in action when your phone's photo app instantly recognizes faces or objects—it was pre-trained on millions of images and then fine-tuned for your specific use.
Many people think transfer learning means the model can learn completely new tasks without any new data. Actually, you usually still need some new, task-specific data to fine-tune the model for the new job.
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. It involves taking the learned features (e.g., from a deep neural network trained on ImageNet) and adapting them to a new, often smaller dataset. This approach leverages the pre-existing knowledge to improve learning efficiency and performance on the target task.