Deep Neural Network Visualization for Interpretable Diagnosis
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Solution Overview
Problem
Existing deep neural networks lack interpretability, making it difficult for developers to understand the reasons behind model predictions and identify systematic causes of misclassification, which hinders model refinement and performance improvement.
Innovation Solution
A visual analytics framework integrating human knowledge through an active learning process with a user interface to train a concept extractor network, allowing users to extract semantically meaningful visual concepts and analyze model errors, using deep embeddings and interactive filters for efficient data labeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep neural networks are used for model predictions, then prediction accuracy is improved, but interpretability deteriorates making it difficult to understand the reasons behind predictions
Solution Approach 1:
The patent introduces concept activation vectors (CAVs) as an intermediary between the deep neural network and the user. CAVs serve as a mediator that translates the internal representations of the neural network into human-interpretable concepts, allowing users to understand what visual concepts the network is using for predictions without sacrificing the network's prediction accuracy
Solution Approach 2:
The patent replaces the need for direct human interpretation of complex neural network operations with an automated computational system. The system automatically extracts and visualizes concept activations, substituting manual analysis of network behavior with an automated process that generates interpretable outputs
2Measurement precision
If developers manually analyze model predictions to understand misclassification causes, then diagnostic accuracy is improved, but time consumption increases
Solution Approach 1:
The patent enables the neural network to self-diagnose its own behavior by automatically generating concept activation visualizations. The system performs self-analysis of its predictions and misclassifications without requiring manual developer intervention, thus maintaining diagnostic accuracy while significantly reducing time consumption
Solution Approach 2:
The patent performs preliminary analysis of model predictions by automatically generating concept activations and visualizations before developers need to review them. This preliminary action prepares diagnostic information in advance, reducing the time developers need to spend on analysis while maintaining accurate diagnostics
3Reliability
If systematic causes of misclassification are identified through manual analysis, then model refinement effectiveness is improved, but developer effort increases
Solution Approach 1:
The system automatically identifies systematic causes of misclassification by analyzing concept activations across multiple predictions. This self-service capability reduces developer effort by performing the analysis automatically while maintaining the effectiveness of model refinement by accurately identifying systematic issues
Solution Approach 2:
The patent implements feedback by visualizing concept activations and their relationship to predictions and misclassifications. This feedback mechanism automatically provides developers with insights into systematic issues, reducing the effort needed to identify problems while maintaining effective model refinement through accurate diagnostic information
Data Source
AI summary
A computer-implemented method includes receiving one or more images from one or more sensors, creating one or more image patches utilizing the one or more images, creating one or more latent representations from the one or more image patches via a neural network, outputting, to a concept extractor network, the one or more latent representations utilizing the one or more image patches, defining one or more scores associated with the one or more latent representations, and outputting one or more scores associated with the one or more image patches utilizing at least the concept extractor network.