Autoencoder Explainability via Reconstruction Error Propagation
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Solution Overview
Problem
Complex deep learning models, such as autoencoders, are challenging to interpret due to their complexity, leading to a need for advanced techniques to explain their decision-making processes, especially in anomaly detection where reconstruction errors require understanding.
Innovation Solution
A novel relevance propagation rule in layer-wise relevance propagation (LRP) for feature attribution-based explanation is introduced, using deep Taylor decomposition to derive a propagation rule for the residual cost function of autoencoders, allowing for attribution of reconstruction errors to input features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If deep learning models (autoencoders) are used for anomaly detection, then detection capability is improved, but model interpretability deteriorates
Solution Approach 1:
The patent segments the autoencoder into distinct functional components: encoding layer, bottleneck layer, and decoding layer. It further segments the explanation process by identifying specific neurons and their corresponding input features that contribute most to reconstruction error. This segmentation allows complex model behavior to be broken down into interpretable units, showing which features caused the anomaly detection while maintaining the full model's detection capability.
Solution Approach 2:
The patent introduces an intermediary explanation layer between the complex autoencoder and the user/analyst. This intermediary takes the form of feature attribution scores and relevance propagation results that translate the autoencoder's internal reconstruction error into human-understandable feature importance rankings. The intermediary bridges the gap between model complexity and interpretability requirements.
2Loss of information
If existing explainability methods are applied to autoencoders, then some explanation is provided, but computational cost increases significantly
Solution Approach 1:
The patent performs preliminary action by pre-computing and storing the autoencoder's layer-wise relevance propagation rules and feature attribution mechanisms during or after training. This preparation allows rapid explanation generation during inference without requiring time-consuming re-computation. The system has already established the computational framework needed for explanation, so actual explanation generation becomes much faster.
Solution Approach 2:
The patent creates a simplified copy or representation of the autoencoder's internal state for explanation purposes. Instead of analyzing the full complex model architecture in real-time, the system uses a streamlined representation that captures essential reconstruction error information and feature attribution relationships. This copying approach maintains explanatory power while dramatically reducing computational requirements for explanation generation.
3Measurement precision
If multiple baseline inputs are used for explanation, then explanation accuracy is improved, but algorithmic complexity increases
Solution Approach 1:
The patent implements self-service by enabling the autoencoder to explain its own reconstruction error using its internal layer-wise relevance propagation mechanisms. The model uses its own pre-computed relevance rules and feature attribution data to generate explanations without requiring external baseline inputs or additional reference models. The autoencoder serves both its detection function and its explanation function through its inherent architecture, eliminating the need for complex multi-baseline comparisons.
Data Source
AI summary
The present invention relates to machine learning (ML) explainability (MLX). Herein are techniques for a novel relevance propagation rule in layer-wise relevance propagation (LRP) for feature attribution-based explanation (ABX) for a reconstructive autoencoder. In an embodiment, a reconstruction layer of a reconstructive neural network in a computer generates a reconstructed tuple that is based on an original tuple that contains many features. A reconstruction residual cost function calculates a reconstruction error that measures a difference between the original tuple and the reconstructed tuple. Applied to the reconstruction error is a novel reconstruction relevance propagation rule that assigns a respective reconstruction relevance to each reconstruction neuron in the reconstruction layer. Based on the reconstruction relevance of the reconstruction neurons, a respective feature relevance of each feature is determined, from which an ABX explanation may be automatically generated.