Posts about domain adaptation.
An overview of proxy-label approaches for semi-supervised learning
While unsupervised learning is still elusive, researchers have made a lot of progress in semi-supervised learning. This post focuses on a particular promising category of semi-supervised learning methods that assign proxy labels to unlabelled data, which are used as targets for learning.
Learning to select data for transfer learning
Domain adaptation methods typically seek to identify features that are shared between the domains or learn representations that are general enough to be useful for both domains. This post discusses a complementary approach to domain adaptation that selects data that is useful for training the model.
Transfer Learning - Machine Learning's Next Frontier
Deep learning models excel at learning from a large number of labeled examples, but typically do not generalize to conditions not seen during training. This post gives an overview of transfer learning, motivates why it warrants our application, and discusses practical applications and methods.