Saliency Visualization via Regional Segmentation and Gain Iteration
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing saliency methods for evaluating and understanding machine-learning models are inefficient due to their reliance on perturbation-based approaches, which require multiple model queries, are slow, and degrade in performance with increasing feature numbers. Additionally, these methods struggle with providing reliable estimates across all perturbations due to the nonlinear nature of neural networks.
Innovation Solution
A computer-implemented method for attribution that segments input data into regions, generates attribution scores for features, determines gain values for regions based on attribution scores, and iteratively adds regions with the greatest gain values to a saliency mask to produce saliency visualizations. This method also includes validation techniques to assess the validity of saliency techniques and performance evaluation methods to assess the quality of saliency maps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If perturbation-based saliency methods are used to directly estimate the impact of features on output, then measurement precision is improved, but productivity deteriorates due to requiring multiple model queries
Solution Approach 1:
The patent segments the input data into multiple regions, allowing the system to compute saliency scores for each region separately. This segmentation enables more efficient processing by reducing the computational burden on individual regions while maintaining overall measurement precision through aggregated regional analysis.
Solution Approach 2:
The patent performs preliminary computations by pre-processing input data into regional segments before the actual saliency calculation. This preliminary action organizes the data structure to facilitate faster subsequent processing, reducing the number of model queries needed while preserving measurement accuracy.
2Measurement precision
If perturbation-based methods are applied to all feature subsets to obtain reliable estimates, then measurement precision is improved, but device complexity increases due to exponential complexity
Solution Approach 1:
By dividing the feature space into regional segments, the patent reduces the combinatorial explosion of feature subsets. Each region can be analyzed independently with fewer perturbations, significantly lowering computational complexity while maintaining reliable saliency estimates through the aggregation of regional results.
Solution Approach 2:
The patent applies perturbation methods selectively to representative regions rather than exhaustively to all possible feature subsets. This partial action approach captures the essential saliency information with significantly reduced computational complexity while maintaining sufficient measurement precision for practical applications.
3Productivity
If region-based segmentation is implemented to improve processing efficiency, then productivity is improved, but measurement precision may deteriorate due to loss of fine-grained feature information
Solution Approach 1:
The patent implements a hierarchical segmentation strategy where input data is divided into regions for efficient processing, but the methodology preserves the ability to trace regional saliency scores back to individual feature contributions. This maintains measurement precision by establishing clear mappings between regional aggregations and underlying features.
Solution Approach 2:
The patent introduces an additional computational dimension by computing both regional saliency scores and feature-level attributions. This multi-dimensional approach allows the system to benefit from the efficiency of region-based processing while simultaneously maintaining precise feature-level measurements through parallel attribution tracking.
Data Source
AI summary
Methods, systems, devices, and tangible non-transitory computer readable media for saliency visualization are provided. The disclosed technology can include receiving a data input including a plurality of features. The data input can be segmented into regions. At least one of the regions can include two or more of the features. Attribution scores can be respectively generated for features of the data input. The attribution scores for each feature can be indicative of a respective saliency of such feature. A respective gain value for each region can be determined over one or more iterations based on the respective attribution scores associated with the features included in the region. Further, at each iteration one or more of the regions with the greatest gain values can be added to a saliency mask. Furthermore, at each iteration a saliency visualization can be produced based on the saliency mask.