Transfer Learning

Transfer learning is a technique in which the knowledge and features learned by a model on one task are applied to efficiently train it on a different but related task. Standard machine learning typically requires large amounts of data, but transfer learning makes it possible to build high-accuracy models even when data is scarce. A classic example is taking a model pre-trained on a large general image dataset (such as ImageNet) and applying it to medical image diagnosis or specific object detection — carrying over the learned weights. Key benefits of transfer learning: • Reduced training time: Feature extraction from the initial stage can be reused, lowering training cost • High accuracy with limited data: Effective learning is possible even when task-specific data is scarce • General-purpose features: The general-purpose features learned in initial training transfer well to other tasks Common approaches to transfer learning include: • Fine-tuning: Partially or fully updating the pre-trained model's weights for the new task • Feature extractor: Using the output of intermediate layers in a pre-trained model as input features for a new model • Domain adaptation: Transferring knowledge between different but related domains Transfer learning is widely applied in fields including healthcare, speech recognition, natural language processing, and image processing — and is an indispensable technology for the practical deployment and broader adoption of AI.