Neural Embedding Clustering for Diverse Vision Data Pruning
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Solution Overview
Problem
The high cost and time-consuming nature of human annotation for large datasets in machine learning-based computer vision tasks, coupled with the scarcity of high-quality, mission-specific, annotated data, necessitates a more efficient method for data pruning to reduce the amount of labeled data required.
Innovation Solution
An Active Learning Python module utilizing OpenCLIP for image embeddings and K-means clustering to create a diverse subset of a dataset by selecting images based on internal diversity metrics, such as cluster radius, to minimize annotation effort.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data pruning is performed using traditional methods (Forgetting Scores, Memorization Scores, EL2N Scores), then model performance can be maintained with fewer samples, but these methods require labeled data which is expensive and time-consuming to obtain
Solution Approach 1:
The patent introduces an intermediary clustering process that operates on unlabeled data to identify representative samples. Instead of directly selecting samples based on labeled performance metrics, the method uses clustering algorithms (K-means, DBSCAN, HDBSCAN) as an intermediary step to group similar images and select diverse representatives from each cluster, eliminating the need for expensive labeled data while maintaining data quality
Solution Approach 2:
The patent replaces the manual annotation process (mechanical human effort) with automated computational methods. By substituting human annotators with algorithmic clustering and diversity-based selection, the system achieves sample selection without requiring time-consuming and expensive human labeling, dramatically improving annotation efficiency
2Quantity of substance
If a large dataset is annotated, then model training data availability increases, but annotation cost and time increase exponentially
Solution Approach 1:
The patent extracts only the most informative and diverse samples from the unlabeled dataset using clustering algorithms. Instead of annotating the entire large dataset, the method identifies and extracts a representative subset of samples that capture the full diversity of the data distribution, significantly reducing annotation time while maintaining training effectiveness
Solution Approach 2:
The patent applies partial action by annotating only a portion of the dataset rather than the complete set. By selecting a carefully curated subset of diverse representative samples through clustering, the method achieves sufficient model training with partial annotation, avoiding the exponential time cost of annotating all available data
3Productivity
If diverse subset selection is performed without clustering, then selection speed increases, but representativeness and quality of the subset decrease
Solution Approach 1:
The patent performs preliminary clustering on the unlabeled data before final sample selection. This preliminary organization of data into meaningful groups based on visual similarity allows for efficient subsequent sampling that guarantees diversity and representativeness, achieving both speed and quality through pre-processed data structure
Data Source
AI summary
The method and system for data pruning use the novel heuristic of weighting the selection of images by an internal diversity metric, such as the radius of the cluster, allowing more images to be sampled from clusters that are more internally diverse. This heuristic is added to improve the overall diversity of the selected images and to prevent the over-representation of similar images. By sampling more images from clusters that are more internally diverse, the approach is able to better represent the overall distribution of the data, improving the quality of the resulting pruned dataset.