Explainable Autoencoders and GANs for Feature Attribution
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Solution Overview
Problem
Existing autoencoder and generative adversarial network architectures lack explainability, making it difficult to trace the input feature space's contribution to the output and providing limited insight into the model's decision-making process.
Innovation Solution
Implement an explainable autoencoder (XAED) and explainable generative adversarial network (XGAN) that incorporate explainable architectures, such as XNNs and INNs, to provide feature attributions and explanations within the model, allowing for a white-box approach to understanding the input-output relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If standard autoencoder or GAN architectures are used, then model performance and reconstruction capability are improved, but explainability and interpretability deteriorate
Solution Approach 1:
The patent segments the neural network into explainable components by introducing separate explanation networks that process different aspects of the input data independently. These explanation networks generate interpretable feature attributions that can be separately analyzed while the main network maintains its high-performance reconstruction capabilities.
Solution Approach 2:
The patent introduces explanation networks as intermediary components between the input data and the main neural network processing. These intermediary networks generate human-interpretable explanations without interfering with the core reconstruction function, acting as a bridge between black-box performance and white-box interpretability.
2Measurement precision
If black-box neural network architectures are used, then reconstruction accuracy is improved, but understanding of feature contributions deteriorates
Solution Approach 1:
The patent implements feedback mechanisms where explanation networks continuously generate feature attribution information that can be used to understand and analyze the decision-making process. This feedback loop provides ongoing insights into feature contributions without compromising the reconstruction accuracy of the main network.
Solution Approach 2:
The neural network architecture performs self-explanation by incorporating explanation networks within the same system. The network generates its own feature attributions and explanations internally, eliminating the need for external interpretation tools and enabling autonomous analysis of feature contributions.
3Loss of information
If external induction steps are added for explainability, then interpretability is improved, but system complexity and processing time deteriorate
Solution Approach 1:
The patent merges the explanation generation capability directly into the neural network architecture by integrating explanation networks with the main processing network. This unified structure eliminates the need for separate external induction steps and reduces overall system complexity while maintaining interpretability.
Solution Approach 2:
The explanation networks perform preliminary analysis of feature contributions during the forward pass, generating explanations before the main reconstruction process completes. This preliminary action provides interpretability information in real-time without requiring additional post-processing steps.
Data Source
AI summary
An exemplary embodiment provides an autoencoder which is explainable. An exemplary autoencoder may explain the degree to which each feature of the input attributed to the output of the system, which may be a compressed data representation. An exemplary embodiment may be used for classification, such as anomaly detection, as well as other scenarios where an autoencoder is input to another machine learning system or when an autoencoder is a component in an end-to-end deep learning architecture. An exemplary embodiment provides an explainable generative adversarial network that adds explainable generation, simulation and discrimination capabilities. The underlying architecture of an exemplary embodiment may be based on an explainable or interpretable neural network, allowing the underlying architecture to be a fully explainable white-box machine learning system.