VAE Latent Space Navigation for Anomaly Detection Explanations
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Solution Overview
Problem
Current anomaly detection methods, particularly in the context of zero-trust networks, face challenges in providing actionable explanations for deep learning models, leading to high computational costs and limited interpretability of variational autoencoder (VAE) models.
Innovation Solution
The proposed solution utilizes the latent space of a VAE to generate actionable explanations by creating synthetic samples that demonstrate the changes needed to transform an anomalous sample into a non-anomalous one, thereby reducing computational costs and enhancing model interpretability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If post-hoc explainability operations are performed using traditional methods, then actionable explanations can be obtained, but computational cost increases significantly
Solution Approach 1:
The patent performs explainability operations during the training phase rather than as post-hoc analysis. By computing attribution signals and generating explanations while the model is being trained, the system obtains actionable explanations without incurring additional computational costs during inference, effectively embedding the explanation generation into the existing training workflow
Solution Approach 2:
The system uses the VAE model's own latent space and internal representations to generate explanations, rather than relying on external post-hoc explanation tools. The attribution signals are derived from the model's own training dynamics and latent variable relationships, allowing the model to explain its own decisions using resources already invested in training
2Measurement precision
If deep learning models are used for anomaly detection, then detection accuracy improves, but interpretability of model decisions deteriorates
Solution Approach 1:
The patent introduces attribution signals as an intermediary mechanism that bridges the gap between the complex internal representations of the VAE and human-understandable explanations. These attribution signals serve as a mediator that translates the model's latent space relationships into interpretable features, allowing stakeholders to understand which input features most influenced the anomaly detection decision
Solution Approach 2:
The system segments the explanation generation into distinct components: latent space encoding, attribution signal computation, and feature-level interpretation. By breaking down the explanation process into these manageable segments, the patent makes the interpretability of deep learning models more systematic and actionable without compromising detection accuracy
3Ease of operation
If synthetic samples are generated through latent space navigation, then actionable explanations are provided, but model complexity increases
Solution Approach 1:
The patent makes the VAE latent space serve multiple functions: it acts as both the encoding space for anomaly detection and as the navigation space for generating actionable explanations. By designing the latent space to be both compressed (for efficient anomaly detection) and structured (for interpretable navigation), the system achieves multi-functionality without requiring separate complex modules for explanation generation
Data Source
AI summary
A framework for extracting actionable explanations using a VAE model. The VAE model operates on a test set of non-anomalous samples. Reconstruction errors are computed based on the VAE's output. The reconstruction errors are used to define a threshold that is usable to determine whether data is anomalous or is non-anomalous. A set of synthetic samples are generated by navigating through a latent space that exists between embeddings of anomalous input and embeddings of anomalous denoised samples. Differences between the synthetic samples and their neighbors are computed. These differences are used to generate a temporal heatmap.