Multi-Source Domain Adaptation Using Approximate Label Matching
Year: 2016
Type: preprint
Abstract: Domain adaptation, and transfer learning more generally, seeks to remedy the problem created when training and testing datasets are generated by different distributions. In this work, we introduce a new unsupervised domain adaptation algorithm for when there are multiple sources available to a learner. Our technique assigns a rough labeling on the target samples, then uses it to learn a transformation that aligns the two datasets before final classification. In this article we give a convenient implementation of our method, show several experiments using it, and compare it to other methods commonly used in the field.
Source: arXiv (Cornell University)
Authors: Jordan T. Ash, Robert E. Schapire
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2280198878