TY - GEN
T1 - Shallow and deep models for domain adaptation problems
AU - Mehrkanoon, Siamak
AU - Blaschko, Matthew B.
AU - Suykens, Johan A.K.
N1 - Funding Information:
This work was supported by • The Postdoctoral Fellowship of the Research Foundation-Flanders (FWO: 12Z1318N). • Research Council KUL: CoE PFV/10/002, PhD/Postdoc grants Flemish Government; FWO: projects: G0A4917N, G.088114N. Siamak Mehrkanoon is Postdoctoral Fellow of the Research Foundation - Flanders (FWO). Matthew Blaschko is a professor at the KU Leuven, Belgium. Johan Suykens is a full professor at the KU Leuven, Belgium.
Publisher Copyright:
© ESANN 2018 - Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning.
PY - 2018
Y1 - 2018
N2 - Manual labeling of sufficient training data for diverse application domains is a costly, laborious task and often prohibitive. Therefore, designing models that can leverage rich labeled data in one domain and be applicable to a different but related domain is highly desirable. In particular, domain adaptation or transfer learning algorithms seek to generalize a model trained in a source domain to a new target domain. Recent years has witnessed increasing interest in these types of models due to their practical importance in real-life applications. In this paper we provide a brief overview of recent techniques with both shallow and deep architectures for domain adaptation models.
AB - Manual labeling of sufficient training data for diverse application domains is a costly, laborious task and often prohibitive. Therefore, designing models that can leverage rich labeled data in one domain and be applicable to a different but related domain is highly desirable. In particular, domain adaptation or transfer learning algorithms seek to generalize a model trained in a source domain to a new target domain. Recent years has witnessed increasing interest in these types of models due to their practical importance in real-life applications. In this paper we provide a brief overview of recent techniques with both shallow and deep architectures for domain adaptation models.
UR - http://www.scopus.com/inward/record.url?scp=85067232316&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85067232316
T3 - ESANN 2018 - Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
SP - 291
EP - 299
BT - ESANN 2018 - Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
PB - i6doc.com publication
T2 - 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2018
Y2 - 25 April 2018 through 27 April 2018
ER -