Non-Negative Bregman Divergence Minimization for Deep Direct Density Ratio Estimation
This work addresses overfitting issues in density ratio estimation for tasks like anomaly detection, but it is incremental as it builds on existing Bregman divergence methods.
The paper tackles the problem of train-loss hacking in Bregman divergence minimization for deep density ratio estimation, proposing a non-negative correction method that improves performance in inlier-based outlier detection.
Density ratio estimation (DRE) is at the core of various machine learning tasks such as anomaly detection and domain adaptation. In existing studies on DRE, methods based on Bregman divergence (BD) minimization have been extensively studied. However, BD minimization when applied with highly flexible models, such as deep neural networks, tends to suffer from what we call train-loss hacking, which is a source of overfitting caused by a typical characteristic of empirical BD estimators. In this paper, to mitigate train-loss hacking, we propose a non-negative correction for empirical BD estimators. Theoretically, we confirm the soundness of the proposed method through a generalization error bound. Through our experiments, the proposed methods show a favorable performance in inlier-based outlier detection.