Transfer Learning Theory

Transfer learning is a machine learning technique where a model trained on one task is adapted to perform a different but related task. Instead of starting from scratch, the model leverages knowledge gained earlier, saving time and resources. For example, a model trained to recognize everyday objects can be fine-tuned to identify specific items like medical images. This approach improves efficiency and often results in better performance, especially when data for the new task is limited. Essentially, transfer learning applies prior learning to accelerate and enhance new learning situations.