A Primer on Transfer Learning

Transfer learning is a machine learning technique that allows a model to apply knowledge gained from solving one set of problems to tackle related problems, thereby improving training speed and reducing the data needed for new tasks. This approach is analogous to reusing code in programming and is particularly effective when the domains of the initial and new problems are similar. For instance, a model trained to recognize dogs can more readily learn to identify specific breeds. Transfer learning involves fine-tuning a model's weights, which can either start from scratch with random values or use pre-existing values from a related task. The technique is beneficial when dealing with large datasets, as it allows models to leverage previously trained weights from datasets like ImageNet, thereby reducing the need for extensive manual labeling. Platforms like Roboflow facilitate the implementation of transfer learning in computer vision by allowing users to use weights from publicly available datasets, enhancing the efficiency and accessibility of model training.