RCA: A Deep Collaborative Autoencoder Approach for Anomaly Detection

Boyang Liu, Ding Wang, Kaixiang Lin, Pang-Ning Tan, Jiayu Zhou

Open source

DOI: 10.24963/ijcai.2021/208
Published: 2021-08
Container: Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence
Publisher: International Joint Conferences on Artificial Intelligence Organization
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.24963/ijcai.2021/208,
  title = {RCA: A Deep Collaborative Autoencoder Approach for Anomaly Detection},
  author = {Boyang Liu and Ding Wang and Kaixiang Lin and Pang-Ning Tan and Jiayu Zhou},
  year = {2021},
  journal = {Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence},
  doi = {10.24963/ijcai.2021/208},
  url = {https://doi.org/10.24963/ijcai.2021/208}
}

RIS

TY  - JOUR
TI  - RCA: A Deep Collaborative Autoencoder Approach for Anomaly Detection
AU  - Boyang Liu
AU  - Ding Wang
AU  - Kaixiang Lin
AU  - Pang-Ning Tan
AU  - Jiayu Zhou
PY  - 2021
JO  - Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence
DO  - 10.24963/ijcai.2021/208
UR  - https://doi.org/10.24963/ijcai.2021/208
ER  -

APA

Liu, B., Wang, D., Lin, K., Tan, P., & Zhou, J. (2021). RCA: A Deep Collaborative Autoencoder Approach for Anomaly Detection. Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. https://doi.org/10.24963/ijcai.2021/208

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crossref · retrieved 2026-09-25T16:17:46.989Z