Model-Agnostic Image Segmentation via Probability Distance Mapping
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Solution Overview
Problem
Current methods lack an agnostic tool for providing insight into the decision-making process of trained statistical models, such as neural networks, which are specific to convolutional neural networks (CNNs) and do not offer broad applicability across different types of models.
Innovation Solution
A mechanism that iteratively modifies input data, calculates new predictions using a trained statistical model, and generates a distance map to represent the impact of each data unit on the prediction, allowing for agnostic analysis and identification of regions of interest.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If CNN-specific methods are used to analyze model decision-making, then analysis capability is improved for convolutional neural networks, but applicability to other statistical models deteriorates
Solution Approach 1:
The patent creates a universal framework that works with any trained statistical model by using model-agnostic perturbation techniques. The method applies general principles of input perturbation and probability distance calculation that are not specific to CNNs, enabling the same analysis approach to work with various model types including decision trees, support vector machines, and other statistical models.
Solution Approach 2:
The patent segments the analysis process into distinct components: perturbation generation, model prediction on perturbed inputs, probability distance calculation, and visualization. This segmentation allows each component to be independently optimized and applied broadly across different model types without requiring model-specific customization.
2Measurement precision
If iterative image modification is performed to analyze each pixel impact, then measurement precision of pixel impact is improved, but computational time increases
Solution Approach 1:
The patent applies partial action by modifying only specific regions of the input image rather than the entire image for each perturbation. This selective modification reduces the number of computations required while still providing meaningful pixel impact analysis, balancing measurement precision with computational efficiency.
Solution Approach 2:
The patent changes the perturbation parameters (magnitude, direction, type of modification) to optimize the balance between measurement precision and computational time. By adjusting these parameters, the system can achieve sufficient pixel impact measurement accuracy without requiring excessive computational resources.
Data Source
AI summary
A mechanism is provided in a data processing system comprising a processor and a memory, the memory comprising instructions executed by the processor to specifically configure the processor to implement a statistical model tool for providing insight into decision making. The statistical model tool applies the statistical model to an input image to generate an original classification probability. An image modification component executing within the statistical model tool iterative modifies each portion of the input image to generate a modified image. The statistical model tool applies the statistical model to the modified image to generate a new classification probability for each portion of the input image. A compare component executing in the statistical model tool compares each new classification probability to the original classification probability to generate a respective probability distance. A distance map generator executing within the statistical model tool generates a distance map data structure based on the probability distances. The distance map data structure represents an impact each portion of the input image has on determining classification probability by the statistical model.