Name
Abstract | ||
|---|---|---|
Unsupervised domain adaptation is emerging as a powerful technique to improve the generalizability of deep learning models to new image domains without using any labeled data in the target domain. In the literature, solutions which perform cross-domain feature-matching (e.g., ADDA), pixel-matching (CycleGAN), and combination of the two (e.g., CyCADA) have been proposed for unsupervised domain adaptation. Many of these approaches make a strong assumption that the source and target label spaces are the same, however in the real-world, this assumption does not hold true. In this paper, we propose a novel solution, FlexAdapt, which extends the state-of-the-art unsupervised domain adaptation approach of CyCADA to scenarios where the label spaces in source and target domains are only partially overlapped. Our solution beats a number of state-of-the-art baseline approaches by as much as 29% in some scenarios, and represent a way forward for applying domain adaptation techniques in the real world. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ICMLA.2019.00155 | ICMLA |
Field | DocType | Citations |
Generalizability theory,Computer science,Domain adaptation,Artificial intelligence,Labeled data,Deep learning,Machine learning,Adversarial system | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Akhil Mathur | 1 | 101 | 15.10 |
| Anton Isopoussu | 2 | 2 | 2.08 |
| Fahim Kawsar | 3 | 909 | 80.24 |
| Nadia Bianchi-Berthouze | 4 | 1239 | 98.61 |
| Nicholas D. Lane | 5 | 4247 | 248.15 |