Deep generative models are challenging the classical methods in the field of anomaly detection nowadays. Every new method provides evidence of outperforming its predecessors, often with contradictory results. The objective of this comparison is twofold: comparison of anomaly detection methods of various paradigms, and identification of sources of variability that can yield different results. The methods were compared on popular tabular and image datasets. While the one class support-vector machine (OC-SVM) had no rival on the tabular datasets, the best results on the image data were obtained either by a feature-matching GAN or a combination of variational autoencoder (VAE) and OC-SVM, depending on the experimental conditions. The main sources of variability that can influence the performance of the methods were identified to be: the range of searched hyper-parameters, the methodology of model selection, and the choice of the anomalous samples. All our code and results are available for download.
翻译:深度基因模型挑战了当今异常现象探测领域的古典方法。 每一个新方法都提供了其前身表现优于以往方法的证据, 往往结果相互矛盾。 比较的目的是双重的: 比较各种范式的异常检测方法, 并查明可产生不同结果的变异性来源。 方法在流行的表格和图像数据集中进行了比较。 虽然一个级支持- 摄像机( OC- SVM) 在表格数据集上没有对手, 图像数据的最佳结果要么是通过特征匹配的 GAN 获得的, 要么是通过变异性自动计算机( VAE) 和 OC- SVM 组合获得的, 取决于实验条件。 能够影响方法性能的主要变异性来源被确定为: 搜索的超参数范围、 模型选择方法和选择异常样品。 我们的所有代码和结果都可以下载。