Batch Active Learning Using Shapley Value Prediction
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Solution Overview
Problem
Current batch active learning methods for neural networks do not effectively consider the contribution of data points to the performance of the model, leading to inefficiencies and increased computational overhead due to the selection of similar or redundant data points, which can result in poor model performance and high resource utilization.
Innovation Solution
The use of Shapley values to predict the contribution of unlabeled data points to the performance of a neural model, allowing for the selection of a diverse subset of data points for labeling based on their predicted contributions, thereby optimizing model performance and reducing computational overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If batch active learning selects data points without considering their contribution to model performance, then the selection process is simple and fast, but the model performance improves slowly and computational resources are wasted on redundant data
Solution Approach 1:
The patent introduces Shapley values as an intermediary mechanism to evaluate and rank the contribution of each data point to model performance. This intermediary system processes the relationship between data points and model outcomes, providing a quantitative basis for selecting the most valuable data points for labeling, thus resolving the contradiction between selecting high-contributing data and maintaining labeling efficiency
Solution Approach 2:
The patent changes the selection parameter from random or uncertainty-based metrics to Shapley value-based contribution metrics. By transforming the selection criterion to measure actual performance contribution, the system identifies data points that provide maximum value per labeling resource spent, resolving the efficiency-performance tradeoff
2Productivity
If batch active learning selects diverse data points without considering contribution, then computational overhead is reduced, but model performance suffers due to selection of redundant or low-value data
Solution Approach 1:
The patent performs preliminary computation of Shapley values to predict data point contributions before the actual labeling process. This preliminary action ranks data points by expected value, allowing the system to pre-select the most promising candidates for labeling, thus ensuring high model performance while maintaining computational efficiency during the active learning loop
Solution Approach 2:
The patent computes Shapley values for a subset or all unlabeled data points to establish a contribution ranking, then selects only the top-k highest-contributing points for labeling. This partial computation approach avoids the excessive computational cost of evaluating all possible data points while still achieving high-performance selection
3Quantity of substance
If all unlabeled data points are labeled, then complete training data is available, but resource utilization increases significantly and time is wasted on labeling low-contribution data
Solution Approach 1:
The patent extracts and identifies the subset of data points with highest Shapley values from the complete unlabeled dataset. By taking out only the most valuable portion of the data for labeling, the system achieves effective model training with a fraction of the total data, thus reducing annotation resources and time while maintaining training data quality and quantity sufficiency
Data Source
AI summary
Some embodiments of the current disclosure disclose methods and systems for batch active learning using the Shapley values of data points. In some embodiments, Shapley values of a first subset of labeled data are used to measure the contributions of the first subset of data to the performance of neural network. Further, a regression model that correlates the first subset of data to their Shapley values is trained to predict the Shapley values of a second subset of data that are unlabeled. A portion of the second subset of data may then be selected for labeling based on the predicted Shapley values.