Name
Title | ||
|---|---|---|
Deep Co-Space: Sample Mining Across Feature Transformation for Semi-Supervised Learning. |
Abstract | ||
|---|---|---|
Aiming at improving the performance of visual classification in a cost-effective manner, this paper proposes an incremental semi-supervised learning paradigm called deep co-space (DCS). Unlike many conventional semi-supervised learning methods usually performed within a fixed feature space, our DCS gradually propagates information from labeled samples to unlabeled ones along with deep feature lear... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TCSVT.2017.2710478 | IEEE Transactions on Circuits and Systems for Video Technology |
Keywords | DocType | Volume |
Semisupervised learning,Training,Visualization,Data models,Feature extraction,Electronic mail,Machine learning | Journal | 28 |
Issue | ISSN | Citations |
10 | 1051-8215 | 3 |
PageRank | References | Authors |
0.41 | 25 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ziliang Chen | 1 | 11 | 4.60 |
| Keze Wang | 2 | 281 | 14.64 |
| xiao wang | 3 | 56 | 8.32 |
| Pai Peng | 4 | 5 | 1.79 |
| ebroul izquierdo | 5 | 1050 | 148.03 |
| Liang Lin | 6 | 3007 | 151.07 |