Enhancing Anomaly Detection via Generating Diversified and Hard-to-distinguish Synthetic Anomalies.

Hyuntae Kim|Changhee Lee


Anthology ID:DBLP:conf/cikm/KimL24
Volume:Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, CIKM 2024, Boise, ID, USA, October 21-25, 2024
Year:2024
Venue:International Conference on Information and Knowledge Management (CIKM)
Publisher:ACM
Pages:1089-1098
URL:https://doi.org/10.1145/3627673.3679623
DOI:https://doi.org/10.1145/3627673.3679623
DBLP:conf/cikm/KimL24
BibTeX:
@inproceedings{kim-2024-enhancing, author = {Hyuntae Kim and Changhee Lee}, editor = {Edoardo Serra and Francesca Spezzano}, title = {{Enhancing Anomaly Detection via Generating Diversified and Hard-to-distinguish Synthetic Anomalies}}, booktitle = {{Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, CIKM 2024, Boise, ID, USA, October 21-25, 2024}}, pages = {1089--1098}, publisher = {ACM}, year = {2024}, url = {https://doi.org/10.1145/3627673.3679623}, doi = {https://doi.org/10.1145/3627673.3679623} }