Image Classification Explanations via Filter-Suppressed Examples
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Solution Overview
Problem
Existing explainability techniques for machine-learned classifiers often provide unintuitive and biased results, especially when applied to datasets with different data statistics, and fail to offer meaningful explanations that are easily understandable by humans or suitable for automated processing.
Innovation Solution
Utilize a generative model, such as a GAN or VAE, to adapt classifier inputs by suppressing specific filters, generating meaningful adaptations that affect classification decisions, allowing for semantically interpretable explanations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If existing explainability techniques are used to visualize per-pixel activation patterns, then classification explanations can be obtained, but the explanations are unintuitive and biased, failing to provide meaningful interpretations for humans or automated processing
Solution Approach 1:
The patent introduces an intermediary optimization process that bridges the gap between raw pixel-level activations and meaningful explanations. By formulating an optimization problem that searches for perturbations maximizing the difference between original and perturbed classifications, the system mediates between the classifier's internal representations and human-understandable explanations, producing boundary crossing examples that reveal decision boundaries in an interpretable manner
Solution Approach 2:
The patent applies parameter changes by systematically perturbing input parameters (pixel values, feature activations) to observe how classification decisions change. By controlling and varying perturbation parameters such as perturbation magnitude and type, the method transforms static activation patterns into dynamic boundary exploration, generating explanations that show how changes in input parameters affect classification outcomes
2Productivity
If machine-learned classifiers are trained on limited datasets, then training time and data requirements are reduced, but the models become over-parametrized and produce false high confidence predictions with poor generalization performance
Solution Approach 1:
The patent applies preliminary action by performing explainability analysis and boundary exploration during or after the training process, before deployment. By generating boundary crossing examples and analyzing decision boundaries in advance, the method enables verification and validation of model behavior prior to full deployment, allowing detection of over-parametrization issues and poor generalization tendencies before they cause problems in production
Solution Approach 2:
The patent implements feedback mechanisms where the explainability analysis results are used to inform model verification and validation. The boundary crossing examples and perturbation analyses provide feedback about model behavior that can be used to assess whether the model has learned appropriate patterns or is relying on spurious correlations, enabling iterative improvement of both model and training process
3Loss of information
If pixel-level perturbations are applied to explain classifications, then some explanation information can be obtained, but the perturbations do not correspond to meaningful concepts and are difficult to understand or process automatically
Solution Approach 1:
The patent applies segmentation by breaking down the explanation task into distinct components: original classification, perturbed classification, boundary identification, and example generation. By segmenting the analysis into these manageable parts, the system transforms overwhelming pixel-level data into structured boundary crossing examples that can be individually interpreted and automatically processed, making the explanation information both comprehensive and understandable
Data Source
AI summary
A computer-implemented method for explaining a classification of one or more classifier inputs by a trained classifier. A generative model is used that generates inputs for the trained classifier. The generative model comprises multiple filters. Generator inputs corresponding to the one or more classifier inputs are obtained, where a generator input causes the generative model to approximately generate the corresponding classifier input. Filter suppression factors are determined for the multiple filters of the generative model. A filter suppression factor for a filter indicates a degree of suppression for a filter output of the filter. The filter suppression factors are determined based on an effect of adapting the classifier inputs according to the filter suppression factors on the classification by the trained classifier. The classification explanation is based on the filter suppression factors.