Topic: "activation-analysis"
Fraunhofer-AISEC/A3 📦
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.
Language: Python - Size: 41 KB - Last synced at: about 2 years ago - Pushed at: almost 3 years ago - Stars: 12 - Forks: 3
r4dl/nerfinternals
Code for "Analyzing the Internals of Neural Radiance Fields". A method for obtaining a density estimate from underlying MLP activations.
Language: Python - Size: 40 MB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 9 - Forks: 0
Fraunhofer-AISEC/DA3D 📦
Double-Adversarial Activation Anomaly Detection
Language: Python - Size: 44.9 KB - Last synced at: about 2 years ago - Pushed at: over 2 years ago - Stars: 1 - Forks: 0
Fraunhofer-AISEC/ARGUE 📦
Anomaly Detection by Recombining Gated Unsupervised Experts
Language: Python - Size: 51.8 KB - Last synced at: about 2 years ago - Pushed at: over 2 years ago - Stars: 0 - Forks: 0