Dataset Description Mapping for Interpretable Domain Shift Analysis
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Solution Overview
Problem
Existing machine learning workflows struggle to interpret and address differences between synthetic and real data, or between correctly and incorrectly predicted data, particularly in understanding domain shifts, without providing actionable insights.
Innovation Solution
A computer-implemented method using multi-modal foundation models to generate element-wise descriptions of datasets, identify common concepts through large language models, and quantify text-data similarities to highlight anomalies, enabling the detection and explanation of differences between datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If multi-modal foundation models are used to generate element-wise descriptions and identify common concepts, then the interpretability and actionability of dataset differences are improved, but the computational complexity and processing time increase
Solution Approach 1:
The patent segments the dataset comparison process into distinct stages: generating element-wise descriptions for individual data elements, identifying common concepts across elements, calculating text-data similarities, and ranking concepts. This segmentation allows the complex task to be broken down into manageable steps that can be processed systematically, improving interpretability while controlling computational complexity through structured processing.
Solution Approach 2:
The patent introduces natural language descriptions as an intermediary layer between the raw dataset elements and the analysis results. By converting data elements into text descriptions and then analyzing relationships between descriptions, the system achieves better interpretability. This intermediary representation allows complex computational tasks to be performed on text rather than raw data, balancing interpretability gains with computational feasibility.
2Measurement precision
If element-wise descriptions and text-data similarity metrics are calculated for all data elements, then the precision in detecting dataset differences is improved, but the processing time and computational resources increase
Solution Approach 1:
The patent calculates text-data similarity metrics for all data elements against common concepts, which may be more computations than strictly necessary. However, this excessive action ensures comprehensive coverage and high precision in detecting differences. The system accepts the computational cost as a trade-off for achieving thorough and accurate comparison results across the entire dataset.
Solution Approach 2:
The patent performs preliminary processing by generating element-wise descriptions for all data elements before conducting the actual comparison analysis. This preliminary action organizes the data in advance, making the subsequent similarity calculations more efficient. By preparing descriptions upfront, the system reduces the computational burden during the main comparison phase, balancing precision with processing time.
3Ease of operation
If common concepts are ranked based on average text-data similarities, then the actionability of the results is improved, but the computational overhead increases
Solution Approach 1:
The patent transforms the raw similarity data into ranked concepts by changing the parameter representation from individual similarity scores to aggregated rankings. This parameter transformation makes the results more actionable by presenting information in a prioritized format that directly indicates which concepts are most relevant for understanding dataset differences, while the computational overhead is limited to straightforward aggregation and sorting operations.
Data Source
AI summary
A device, a datastructure, and a computer implemented method for digital content processing. The method includes providing a first dataset; providing a second dataset; wherein a digital content of a respective element of the elements of the first and second datasets include a digital image or a digital audio signal; generating, with a data-to-text model, a first set of descriptions, wherein the first set comprises an element-wise description of the elements of the first dataset, wherein the description of the respective element of the first dataset is determined depending on the content of the respective element of the first dataset; generating, with the data-to-text model, a second set of descriptions, wherein the second set comprises an element-wise description of the elements of the second dataset, wherein the description of the respective element of the second dataset is determined depending on the content of the respective element of the second dataset.