Generative Adversarial Active Learning for Unsupervised Outlier Detection

@article{Liu2020GenerativeAA,
  title={Generative Adversarial Active Learning for Unsupervised Outlier Detection},
  author={Yezheng Liu and Zhe Li and Chong Zhou and Yuanchun Jiang and Jianshan Sun and M. Wang and Xiangnan He},
  journal={IEEE Transactions on Knowledge and Data Engineering},
  year={2020},
  volume={32},
  pages={1517-1528}
}
Outlier detection is an important topic in machine learning and has been used in a wide range of applications. [] Key Method To address this, we propose a novel Single-Objective Generative Adversarial Active Learning (SO-GAAL) method for outlier detection, which can directly generate informative potential outliers based on the mini-max game between a generator and a discriminator.

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