Data Slice Rule Generation for AI Model Training
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Solution Overview
Problem
Current data generation methods fail to effectively capture the distribution of real-life data, leading to inadequate training and testing of machine learning models, especially in low-density subspaces, resulting in overfitting and poor performance in extreme cases.
Innovation Solution
A system that generates data slices based on data observations, using data valuation algorithms like Shapley values to identify high-value data points and create rules for generating instances of data that cover underrepresented subspaces, thereby improving model performance across various data modalities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If current data generation methods are used, then data can be generated quickly, but the distribution of real-life data cannot be effectively captured
Solution Approach 1:
The patent segments the data generation process into multiple stages: (1) receiving a dataset with labeled data points, (2) partitioning the dataset into training and validation sets, (3) computing data values using valuation algorithms, (4) generating data slices based on high-value data points, and (5) generating synthetic data instances from these slices. This segmentation allows the system to maintain high productivity while improving distribution accuracy through targeted sampling of valuable data regions.
Solution Approach 2:
The patent changes the parameter of data value by computing valuation metrics (such as Shapley values) for each data point. This parameter change enables the system to identify and prioritize high-value data points that are most representative of the underlying data distribution, thereby generating more reliable synthetic data while maintaining efficient generation speeds.
2Reliability
If data generation focuses on high-value data points, then model performance in low-density subspaces improves, but the complexity of data valuation and slice rule generation increases
Solution Approach 1:
The patent performs preliminary actions by pre-computing data values for all data points using valuation algorithms before generating synthetic data. This preliminary valuation step identifies high-value data points in advance, allowing the subsequent data slice generation to focus only on these valuable regions. This approach improves model performance in low-density subspaces while managing complexity by preparing valuation information beforehand.
Solution Approach 2:
The patent creates simplified copies of high-value data regions through data slices. Instead of working with the entire complex dataset, the system extracts and copies only the essential characteristics of high-value data points into data slices, which then serve as templates for generating synthetic data instances. This copying approach reduces complexity while preserving the valuable patterns needed for improving corner case performance.
3Reliability
If data slices are generated from high-value data points, then coverage of underrepresented subspaces increases, but the time required for data valuation and slice generation increases
Solution Approach 1:
The patent performs data valuation as a preliminary action before slice generation, computing values for all data points in advance. This allows the system to efficiently identify high-value data points and generate data slices without repeated valuation computations during the slicing process, thereby reducing overall time loss while improving coverage of underrepresented subspaces.
Solution Approach 2:
The patent applies local quality by focusing computational resources on evaluating and generating data slices from high-value data points specifically, rather than uniformly processing all data points. This localized approach improves coverage of low-density subspaces by concentrating efforts on the most valuable regions while reducing overall processing time by not expending equal resources on all data points.
Data Source
AI summary
An example system includes a processor to receive a data set. The processor can generate a data slice rule based on a data observation for a data point in the data set. The processor can generate an instance of data based on the generated data slice rule.