Neural Network Filter Identification for Explainable Heatmaps
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Solution Overview
Problem
Current machine learning techniques lack effective methods to provide standardized explanations for neural network predictions, leading to subjective interpretations and potential biases, especially in applications like autonomous driving and law enforcement, where model explainability is crucial for trust and accuracy.
Innovation Solution
A computer-implemented method to identify filters within a trained neural network that determine explainability by iteratively selecting and combining filters to generate an explanation heatmap similar to annotated input images, using similarity metrics like intersection-over-union and F1 score, to ensure consistent and accurate explanations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional black-box or white-box explanation methods are used for neural network predictions, then explanations can be generated, but the explanations are subjective and lack standardization
Solution Approach 1:
The patent transforms subjective explanation methods into objective measurements by changing the parameter of explanation evaluation from human interpretation to quantitative similarity metrics. By computing similarity between explanation heatmaps and annotated reference images using metrics like intersection-over-union and F1 score, the system converts subjective explanations into measurable, standardized parameters that can be objectively evaluated and compared.
2Productivity
If neural networks are trained on limited datasets, then training time and resources are reduced, but the models become overparametrized and produce biased predictions
Solution Approach 1:
The patent implements feedback by using annotated reference images and similarity metrics to evaluate whether the neural network's explanation heatmaps align with expected patterns. This feedback loop allows the system to identify biased or overfit models and adjust training accordingly, improving generalization without requiring excessively large datasets. The feedback mechanism provides quantitative guidance for model improvement.
3Reliability
If standardization of explanations is implemented, then model verification and validation are improved, but the complexity of the explanation evaluation process increases
Solution Approach 1:
The patent uses copying by creating standardized reference explanations (annotated reference images) that represent ground truth or expected patterns. These reference copies serve as benchmarks against which actual model explanations are compared. By copying established annotation standards into the evaluation process, the system achieves standardization without requiring complex custom evaluation logic for each case.
4Measurement precision
If iterative filter selection with similarity metrics is used to identify explainable filters, then explanation accuracy is improved, but computational time and resources increase
Solution Approach 1:
The patent applies partial action by selectively evaluating only the most promising filters through iterative selection based on similarity metrics, rather than exhaustively analyzing all filters in the neural network. The process stops when sufficient explanation accuracy is achieved (when similarity threshold is met), avoiding unnecessary computational expenditure on already-sufficient or irrelevant filters.
Data Source
AI summary
A computer-implemented method of identifying filters for use in determining explainability of a trained neural network. The method comprises obtaining a dataset comprising the input image and an annotation of an input image, the annotation indicating at least one part of the input image which is relevant for inferring classification of the input image, determining an explanation filter set by iteratively: selecting a filter of the plurality of filters; adding the filter to the explanation filter set; computing an explanation heatmap for the input image by resizing and combining an output of each filter in the explanation filter set to obtain the explanation heatmap, the explanation heatmap having a spatial resolution of the input image; and computing a similarity metric by comparing the explanation heatmap to the annotation of the input image; until the similarity metric is greater than or equal to a similarity threshold; and outputting the explanation filter set.