🤖 AI Summary
To address insufficient sensitivity to unseen anomalies in unsupervised anomaly detection, this paper proposes a two-stage light-supervision enhancement framework. First, a variational autoencoder (VAE) is pretrained unsupervisedly on normal data; second, it undergoes supervised fine-tuning using only a small number of known anomaly samples, guided by reconstruction error minimization. Crucially, this work introduces minimal anomaly supervision—requiring labels *only* for anomalies, with no normal-class labels—directly into the VAE reconstruction objective, thereby significantly improving generalization to previously unseen anomalies. Extensive evaluation across four heterogeneous benchmarks—MNIST, CICIDS, LHCO2020, and SMEFT—demonstrates superior normal/anomalous separation. Notably, on the Higgs event momentum-shift detection task, sensitivity improves markedly, validating the efficacy of few-shot anomaly supervision in enhancing unsupervised models. The approach is particularly suited for high-precision, sensitivity-critical domains such as particle physics detection and cybersecurity.
📝 Abstract
We present a refined version of the Anomaly Awareness framework for enhancing unsupervised anomaly detection. Our approach introduces minimal supervision into Variational Autoencoders (VAEs) through a two-stage training strategy: the model is first trained in an unsupervised manner on background data, and then fine-tuned using a small sample of labeled anomalies to encourage larger reconstruction errors for anomalous samples. We validate the method across diverse domains, including the MNIST dataset with synthetic anomalies, network intrusion data from the CICIDS benchmark, collider physics data from the LHCO2020 dataset, and simulated events from the Standard Model Effective Field Theory (SMEFT). The latter provides a realistic example of subtle kinematic deviations in Higgs boson production. In all cases, the model demonstrates improved sensitivity to unseen anomalies, achieving better separation between normal and anomalous samples. These results indicate that even limited anomaly information, when incorporated through targeted fine-tuning, can substantially improve the generalization and performance of unsupervised models for anomaly detection.