Transfer Learning: Domain Adaptation Strategies for Applying Models Across Related Data Distributions
Building high-performing machine learning models traditionally required large, well-labelled datasets for every new problem. In reality, such data is often expensive, slow to acquire, or incomplete. Transfer learning addresses this challenge by allowing knowledge learned from one domain to be reused in another, related domain. Domain adaptation, a key subset of transfer learning, focuses on […]
