Transfer Learning

Transfer learning is a machine learning paradigm where a model trained on one task or dataset is repurposed as the starting point for a model on a different task. Rather than learning from scratch, the model transfers its learned representations, patterns, and knowledge to the new domain, typically requiring far less data and computation to achieve strong performance. This approach has become the dominant paradigm in modern AI, underpinning the foundation model ecosystem.

The theoretical basis for transfer learning is that lower-level features learned by neural networks, such as edge detection in vision models or syntactic patterns in language models, are broadly applicable across tasks and domains. By preserving these general representations and only adapting higher-level task-specific layers, models can leverage the expensive pre-training investment across many applications.

Common transfer learning approaches include feature extraction, where pre-trained model representations are used as fixed inputs to a new classifier; full fine-tuning, where all model parameters are updated on the new task; and parameter-efficient fine-tuning methods like LoRA that update only a small subset of parameters. The choice depends on the similarity between source and target tasks, the amount of available target data, and computational constraints.

Transfer learning is particularly valuable in enterprise settings where labeled data for specific tasks is limited or expensive to obtain. A foundation model pre-trained on general web text can be adapted with just hundreds or thousands of examples to perform specialized tasks like legal document classification, medical report summarization, or financial sentiment analysis. The effectiveness of transfer depends on the relevance of the source domain to the target task, the quality of the pre-trained representations, and the care taken in the adaptation process to avoid catastrophic forgetting of useful pre-trained knowledge.