There is this interesting paper Efficient GAN-based anomaly detection. To evaluate the anomaly detection, they use the following experimental setting
MNIST: We generated 10 different datasets from MNIST by successively making each digit class an anomaly and treating the remaining 9 digits as normal examples. The training set consists of 80% of the normal data and the test set consists of the remaining 20% of normal data and all of the anomalous data. All models were trained only with normal data and tested with both normal and anomalous data.
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有这篇有趣的论文有效的基于GAN的异常检测。
为了评估异常检测，他们使用以下实验设置
There is this interesting paper Efficient GAN-based anomaly detection.
To evaluate the anomaly detection, they use the following experimental setting