$\text{A}^3$: Activation Anomaly Analysis
This work addresses anomaly detection for inspecting large amounts of data across various applications, but it appears incremental as it builds on existing coverage-guided analysis methods.
The paper tackles the problem of anomaly detection by using hidden activation values to distinguish normal from anomalous samples, achieving strong results that surpass current baseline methods on common datasets.
Inspired by recent advances in coverage-guided analysis of neural networks, we propose a novel anomaly detection method. We show that the hidden activation values contain information useful to distinguish between normal and anomalous samples. Our approach combines three neural networks in a purely data-driven end-to-end model. Based on the activation values in the target network, the alarm network decides if the given sample is normal. Thanks to the anomaly network, our method even works in strict semi-supervised settings. Strong anomaly detection results are achieved on common data sets surpassing current baseline methods. Our semi-supervised anomaly detection method allows to inspect large amounts of data for anomalies across various applications.