Name
Abstract | ||
|---|---|---|
One of the main challenges for developing visual recognition systems working in the wild is to devise computational models immune from the domain shift problem, i.e., accurate when test data are drawn from a (slightly) different data distribution than training samples. In the last decade, several research efforts have been devoted to devise algorithmic solutions for this issue. Recent attempts to ... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TPAMI.2020.3001338 | IEEE Transactions on Pattern Analysis and Machine Intelligence |
Keywords | DocType | Volume |
Deep learning,Adaptation models,Computer architecture,Training,Visualization,Entropy,Data models | Journal | 43 |
Issue | ISSN | Citations |
12 | 0162-8828 | 0 |
PageRank | References | Authors |
0.34 | 7 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fabio Maria Carlucci | 1 | 23 | 3.39 |
| Lorenzo Porzi | 2 | 120 | 11.79 |
| Barbara Caputo | 3 | 3298 | 201.26 |
| Elisa Ricci 0002 | 4 | 1393 | 73.75 |
| Samuel Rota Bulò | 5 | 564 | 33.69 |