Transfer-Learning-Library

Why we included this project

Models trained on one dataset often stumble when the data shifts. This library collects the transfer learning techniques people reach for in that situation, from domain alignment and self-training to regularization and data reweighting, behind a PyTorch API that follows torchvision conventions, so it feels familiar if you already work in that ecosystem. Beyond the classic unsupervised domain adaptation methods, it covers fine-tuning, out-of-distribution generalization, and semi-supervised learning, each with runnable examples spanning classification, detection, segmentation, and keypoint tasks. Researchers get clean reference implementations of published algorithms; engineers get building blocks they can drop into a project without reimplementing everything from scratch.