Name
Abstract | ||
|---|---|---|
We address the problem of domain adaptation (DA) from one or multiple source domains to a target domain. Most of the existing DA methods assume that source data is largely available. Such an assumption rarely holds in real applications, for both technical and legal reasons. More realistic are situations where source domain observations become quickly unavailable, but only some domain representatives can be retained, either as source instances or as their aggregation. In this paper therefore we focus on the Domain Specific Class Means (DSCM) classifier [5] that can handle such scenario and we combine it with the sMDA framework [4]. We show, on a variety of datasets and tasks, that the method can be applied successfully even when no labeled target is available and also that it can provide performance comparable to the case where dense knowledge (all source data) is available. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/ICCVW.2015.20 | ICCV Workshops |
Keywords | Field | DocType |
adapted domain specific class means classifier,source data,sMDA framework,DSCM classifier,unsupervised stacked marginalized denoising autoencoders | Noise reduction,Pattern recognition,Computer science,Source data,Domain adaptation,Noise level,Speech recognition,Feature extraction,Artificial intelligence,Classifier (linguistics) | Conference |
Volume | Issue | Citations |
2015 | 1 | 2 |
PageRank | References | Authors |
0.36 | 19 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gabriela Csurka | 1 | 972 | 85.08 |
| Boris Chidlovskii | 2 | 411 | 52.58 |
| Stephane Clinchant | 3 | 102 | 10.05 |