partial update of alad

README.rst

@@ -371,6 +371,7 @@ Neural Networks      SO_GAAL             Single-Objective Generative Adversarial
 Neural Networks      MO_GAAL             Multiple-Objective Generative Adversarial Active Learning                                               2019   [#Liu2019Generative]_
 Neural Networks      DeepSVDD            Deep One-Class Classification                                                                           2018   [#Ruff2018Deep]_
 Neural Networks      AnoGAN              Anomaly Detection with Generative Adversarial Networks                                                  2017   [#Schlegl2017Unsupervised]_
+Neural Networks      ALAD                Adversarially learned anomaly detection                                                                 2018   [#Zenati2018Adversarially]_
 Graph-based          R-Graph             Outlier detection by R-graph                                                                            2017   [#You2017Provable]_
 Graph-based          LUNAR               LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networks                               2022   [#Goodge2022Lunar]_
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@@ -613,6 +614,8 @@ Reference
 
 .. [#You2017Provable] You, C., Robinson, D.P. and Vidal, R., 2017. Provable self-representation based outlier detection in a union of subspaces. In Proceedings of the IEEE conference on computer vision and pattern recognition.
 
+.. [#Zenati2018Adversarially] Zenati, H., Romain, M., Foo, C.S., Lecouat, B. and Chandrasekhar, V., 2018, November. Adversarially learned anomaly detection. In 2018 IEEE International conference on data mining (ICDM) (pp. 727-736). IEEE.
+
 .. [#Zhao2018XGBOD] Zhao, Y. and Hryniewicki, M.K. XGBOD: Improving Supervised Outlier Detection with Unsupervised Representation Learning. *IEEE International Joint Conference on Neural Networks*\ , 2018.
 
 .. [#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.

docs/index.rst

@@ -207,6 +207,7 @@ Neural Networks      SO_GAAL           Single-Objective Generative Adversarial A
 Neural Networks      MO_GAAL           Multiple-Objective Generative Adversarial Active Learning                                               2019   :class:`pyod.models.mo_gaal.MO_GAAL`                 :cite:`a-liu2019generative`
 Neural Networks      DeepSVDD          Deep One-Class Classification                                                                           2018   :class:`pyod.models.deep_svdd.DeepSVDD`              :cite:`a-ruff2018deepsvdd`
 Neural Networks      AnoGAN            Anomaly Detection with Generative Adversarial Networks                                                  2017   :class:`pyod.models.anogan.AnoGAN`                   :cite:`a-schlegl2017unsupervised`
+Neural Networks      ALAD                Adversarially learned anomaly detection                                                               2018   :class:`pyod.models.alad.ALAD`                       :cite:`a-zenati2018adversarially`
 Graph-based          R-Graph           Outlier detection by R-graph                                                                            2017   :class:`pyod.models.rgraph.RGraph`                   :cite:`you2017provable`
 Graph-based          LUNAR             LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networks                               2022   :class:`pyod.models.lunar.LUNAR`                     :cite:`a-goodge2022lunar`
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