Generative Adversarial Active Learning for Unsupervised Outlier Detection
Generative Adversarial Active Learning for Unsupervised Outlier Detection
Outlier detection is an important topic in machine learning and has been used in a wide range of applications. In this paper, we approach outlier detection as a binary-classification issue by sampling potential outliers from a uniform reference distribution. However, due to the sparsity of data in high-dimensional space, a …