Image Embedding Tree Sampling for Diverse Dataset Selection
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Solution Overview
Problem
Conventional data sampling techniques for machine learning models often result in lower predictive performance and bias due to the exclusion of unique or outlier data points, and active learning methods are computationally complex and resource-intensive.
Innovation Solution
A technique involving the generation of a tree structure with embeddings of data points, allowing for efficient sampling of a diverse subset of data points by traversing the tree, reducing duplicates and increasing uniqueness in the sampled dataset.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If random sampling is used to select data points for labeling, then the sampled data distribution is similar to the original dataset, but unique or outlier data points are excluded resulting in lower predictive performance and bias
Solution Approach 1:
The patent replaces the mechanical random sampling process with an information-theoretic approach using embeddings and mutual information calculation. Instead of randomly selecting data points, the system computes embeddings for all data points, calculates mutual information between pairs of data points based on their embedding distances, and selects data points that maximize information diversity. This substitution ensures unique and outlier data points are captured while maintaining computational feasibility through efficient mutual information estimation.
2Reliability
If active learning techniques are used to increase diversity in labeled data, then the impact on predictive performance is maximized, but the computational complexity and resource requirements increase significantly
Solution Approach 1:
The patent extracts only the essential information needed for diverse sampling by computing mutual information between data point pairs and selecting those with highest information content. Rather than implementing full active learning pipelines with iterative model training and complex heuristic computations, the method extracts the key diversity signal from embeddings and uses it to select a diverse subset for labeling. This extraction approach achieves diversity maximization with significantly reduced computational overhead.
Solution Approach 2:
The patent performs partial active learning by computing mutual information for all data point pairs (excessive in scope) but then selects only the top-k pairs with highest mutual information (partial in execution). This allows the system to capture the full diversity structure of the dataset through complete mutual information calculation, then apply only the necessary subset for labeling, balancing computational thoroughness with practical efficiency.
3Loss of information
If pairwise distances are computed between all possible pairs of data points to select diverse samples, then diversity in labeled data is optimized, but the computation becomes infeasible for large datasets
Solution Approach 1:
The patent segments the computational task by first computing embeddings for all data points independently, then calculating mutual information in a structured manner. The method divides the dataset into manageable portions for embedding computation, then uses efficient distance metrics and mutual information formulas to process pairs systematically. This segmentation allows the system to handle large datasets by breaking down the infeasible O(n²) pairwise computation into staged processing with optimized memory usage and computation patterns.
Data Source
AI summary
One embodiment of the present invention sets forth a technique for sampling from a dataset comprises. The technique includes determining a plurality of embeddings for a plurality of objects depicted in a plurality of images in the dataset. The technique also includes populating a tree structure with the plurality of embeddings by generating a first node that stores a first set of embeddings and generating a first plurality of nodes as children of the first node, where each node included in the first plurality of nodes stores a different subset of embeddings included in the first set of embeddings. The technique further includes sampling a subset of embeddings from the plurality of embeddings via a traversal of the tree structure and generating a sampled dataset that includes a subset of images based on the subset of embeddings and a number of images to be included in the sampled dataset.