Transfer Learning

Transfer Learning is a machine learning paradigm that leverages knowledge acquired from one task or domain to improve performance on a related but different task, reducing training time and data requirements while achieving superior results. This approach utilizes pre-trained models as starting points, transferring learned features, representations, and patterns to new applications rather than training from scratch. Transfer learning proves particularly effective when target tasks have limited training data or computational resources, enabling practitioners to benefit from large-scale pre-training investments. The technique encompasses various strategies including feature extraction, where pre-trained layers remain frozen, and fine-tuning, where model parameters are adjusted for specific tasks. Modern transfer learning implementations support cross-domain adaptation, multi-task learning scenarios, and zero-shot transfer capabilities that enable models to generalize across diverse applications while maintaining robust performance and reducing development costs significantly.