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} }