Name
Title | ||
|---|---|---|
Improving Transferability of Domain Adaptation Networks Through Domain Alignment Layers |
Abstract | ||
|---|---|---|
Deep learning (DL) has been the primary approach used in various computer vision tasks due to its relevant results achieved on many tasks. However, on real-world scenarios with partially or no labeled data, DL methods are also prone to the well-known domain shift problem. Multi-source unsupervised domain adaptation (MSDA) aims at learning a predictor for an unlabeled domain by assigning weak knowl... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/SIBGRAPI54419.2021.00031 | 2021 34th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) |
Keywords | DocType | ISSN |
Graphics,Deep learning,Computer vision,Adaptation models,Predictive models,Feature extraction,Robustness | Conference | 1530-1834 |
ISBN | Citations | PageRank |
978-1-6654-2354-0 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lucas Fernando Alvarenga e Silva | 1 | 0 | 0.34 |
| Daniel Carlos Guimarães Pedronette | 2 | 304 | 25.47 |
| Fábio Augusto Faria | 3 | 0 | 0.34 |
| João Paulo Papa | 4 | 278 | 44.60 |
| Jurandy Almeida | 5 | 431 | 35.15 |