Can Adversarial Training benefit Trajectory Representation?: An Investigation on Robustness for Trajectory Similarity Computation.
Quanliang Jing|Shuo Liu|Xinxin Fan|Jingwei Li|Di Yao|Baoli Wang|Jingping Bi
| Anthology ID: | DBLP:conf/cikm/JingLFLYWB22 |
|---|---|
| Volume: | Proceedings of the 31st ACM International Conference on Information & Knowledge Management, Atlanta, GA, USA, October 17-21, 2022 |
| Year: | 2022 |
| Venue: | International Conference on Information and Knowledge Management (CIKM) |
| Publisher: | ACM |
| Pages: | 905-914 |
| URL: | https://doi.org/10.1145/3511808.3557250 |
| DOI: | https://doi.org/10.1145/3511808.3557250 |
| DBLP: | conf/cikm/JingLFLYWB22 |
| BibTeX: |
@inproceedings{jing-2022-adversarial,
author = {Quanliang Jing and
Shuo Liu and
Xinxin Fan and
Jingwei Li and
Di Yao and
Baoli Wang and
Jingping Bi},
editor = {Mohammad Al Hasan and
Li Xiong},
title = {{Can Adversarial Training benefit Trajectory Representation?: An Investigation on Robustness for Trajectory Similarity Computation}},
booktitle = {{Proceedings of the 31st ACM International Conference on Information & Knowledge Management, Atlanta, GA, USA, October 17-21, 2022}},
pages = {905--914},
publisher = {ACM},
year = {2022},
url = {https://doi.org/10.1145/3511808.3557250},
doi = {https://doi.org/10.1145/3511808.3557250}
}