Name
Title | ||
|---|---|---|
Discovering unknowns: Context-enhanced anomaly detection for curiosity-driven autonomous underwater exploration |
Abstract | ||
|---|---|---|
•Anomaly detection for unknowns towards to autonomous underwater exploration.•Autoencoder and autoregressive network to identify anomalies in unstructured dynamic underwater moving views.•Novel context-enhanced autoregressive network to learn feature dependence.•Patch learning paradigm to build an accurate latent feature space.•Validation on two benchmarks, simulation, and real data, outperforms state-of-arts. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.patcog.2022.108860 | Pattern Recognition |
Keywords | DocType | Volume |
Anomaly detection,Learning unknown objects,Deep learning autoencoder,Autonomous underwater robotics | Journal | 131 |
Issue | ISSN | Citations |
1 | 0031-3203 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yang Zhou | 1 | 0 | 0.34 |
| Baihua Li | 2 | 176 | 21.71 |
| Jiangtao Wang | 3 | 0 | 0.34 |
| Emanuele Rocco | 4 | 0 | 0.34 |
| Qinggang Meng | 5 | 273 | 23.54 |