add FEAWAD

README.rst

@@ -418,6 +418,7 @@ Paper Title
 Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection  SIGKDD                        2018   [#Pang2018Learning]_          `[PDF] <https://arxiv.org/pdf/1806.04808.pdf>`_
 Learning representations for outlier detection on a budget                                          Preprint                      2015   [#Micenkova2015Learning]_     `[PDF] <https://arxiv.org/pdf/1507.08104.pdf>`_
 XGBOD: improving supervised outlier detection with unsupervised representation learning             IJCNN                         2018   [#Zhao2018Xgbod]_             `[PDF] <http://www.andrew.cmu.edu/user/yuezhao2/papers/18-ijcnn-xgbod.pdf>`_
+Feature Encoding With Autoencoders for Weakly Supervised Anomaly Detection                          TNNLS                         2021   [#Zhou2021Feature]_           `[PDF] <https://arxiv.org/pdf/2105.10500.pdf>`_, `[Code] <https://github.com/yj-zhou/Feature_Encoding_with_AutoEncoders_for_Weakly-supervised_Anomaly_Detection>`_
 ==================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================
 
 
@@ -853,6 +854,8 @@ References
 
 .. [#Zhou2019AnomalyNet] Zhou, J.T., Du, J., Zhu, H., Peng, X., Liu, Y. and Goh, R.S.M., 2019. AnomalyNet: An anomaly detection network for video surveillance. *IEEE Transactions on Information Forensics and Security*.
 
+.. [#Zhou2021Feature] Zhou, Y., Song, X., Zhang, Y., Liu, F., Zhu, C., & Liu, L. (2021). Feature encoding with autoencoders for weakly supervised anomaly detection. IEEE Transactions on Neural Networks and Learning Systems, 33(6), 2454-2465.
+
 .. [#Zhu2019Tripartite] Zhu, Y. and Yang, K., 2019. Tripartite Active Learning for Interactive Anomaly Discovery. *IEEE Access*.
 
 .. [#Zimek2012A] Zimek, A., Schubert, E. and Kriegel, H.P., 2012. A survey on unsupervised outlier detection in high‐dimensional numerical data. *Statistical Analysis and Data Mining: The ASA Data Science Journal*\ , 5(5), pp.363-387.

README_CN.rst

@@ -304,6 +304,7 @@ Paper Title
 Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection  SIGKDD                        2018   [#Pang2018Learning]_          `[PDF] <https://arxiv.org/pdf/1806.04808.pdf>`_
 Learning representations for outlier detection on a budget                                          Preprint                      2015   [#Micenkova2015Learning]_     `[PDF] <https://arxiv.org/pdf/1507.08104.pdf>`_
 XGBOD: improving supervised outlier detection with unsupervised representation learning             IJCNN                         2018   [#Zhao2018Xgbod]_             `[PDF] <https://www.yuezhao.me/s/edited_XGBOD.pdf>`_
+Feature Encoding With Autoencoders for Weakly Supervised Anomaly Detection                          TNNLS                         2021   [#Zhou2021Feature]_           `[PDF] <https://arxiv.org/pdf/2105.10500.pdf>`_, `[Code] <https://github.com/yj-zhou/Feature_Encoding_with_AutoEncoders_for_Weakly-supervised_Anomaly_Detection>`_
 ==================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================
 
 
@@ -548,6 +549,8 @@ References
 
 .. [#Zhao2019LSCP] Zhao, Y., Nasrullah, Z., Hryniewicki, M.K. and Li, Z., 2019, May. LSCP: Locally selective combination in parallel outlier ensembles. In *Proceedings of the 2019 SIAM International Conference on Data Mining (SDM)*, pp. 585-593. Society for Industrial and Applied Mathematics.
 
+.. [#Zhou2021Feature] Zhou, Y., Song, X., Zhang, Y., Liu, F., Zhu, C., & Liu, L. (2021). Feature encoding with autoencoders for weakly supervised anomaly detection. IEEE Transactions on Neural Networks and Learning Systems, 33(6), 2454-2465.
+
 .. [#Zhu2019Tripartite] Zhu, Y. and Yang, K., 2019. Tripartite Active Learning for Interactive Anomaly Discovery. *IEEE Access*.
 
 .. [#Zimek2012A] Zimek, A., Schubert, E. and Kriegel, H.P., 2012. A survey on unsupervised outlier detection in high‐dimensional numerical data. *Statistical Analysis and Data Mining: The ASA Data Science Journal*\ , 5(5), pp.363-387.