Most approaches to dense anomaly detection rely on generative modeling or on discriminative methods that train with negative data. We consider a recent hybrid method that optimizes the same shared representation according to cross-entropy of the discriminative predictions, and negative log likelihood of the predicted energy-based density. We extend that work with a jointly trained generative flow that samples synthetic negatives at the border of the inlier distribution. The proposed extension provides potential to learn the hybrid method without real negative data. Our experiments analyze the impact of training with synthetic negative data and validate contribution of the energy-based density during training and evaluation.

翻译：大多数密集异常检测方法依赖生成建模或使用负样本训练的判别方法。我们考虑一种近期提出的混合方法，该方法通过判别预测的交叉熵和预测能量密度的负对数似然来优化同一共享表征。我们通过联合训练生成流对该工作进行扩展，该生成流在内点分布边界处采样合成负样本。所提出的扩展方法提供了无需真实负样本即可学习混合模型的潜力。我们的实验分析了使用合成负样本训练的影响，并验证了训练和评估过程中基于能量的密度的贡献。