Embedding Cluster Label Review for Consistent AI Data Labeling
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Solution Overview
Problem
Existing artificial intelligence-based data labeling processes are hindered by the need for large amounts of high-quality training data, subjective judgment, domain shifts, and noise, leading to inconsistent and biased results.
Innovation Solution
A hybrid data labeling approach using embedding clusters to identify and prioritize data labels for review, ensuring consistency and reducing bias by applying an independent data labeling routine based on cluster analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a hybrid data labeling approach is used to improve labeling capacity, then productivity increases, but label consistency deteriorates
Solution Approach 1:
The patent introduces embedding clusters as an intermediary mechanism between multiple data labeling routines. These clusters group similar data samples together based on their embedding representations, and serve as the basis for determining which samples need additional labeling review. This intermediary structure enables coordination between different labeling routines while maintaining overall label consistency.
Solution Approach 2:
The system implements a feedback mechanism where the results from multiple data labeling routines are analyzed through cluster characteristics, and this information feeds back into determining which samples require additional labeling. The feedback loop continuously refines the labeling process by identifying inconsistent labels through cluster analysis and directing them for review by additional labeling routines.
2Productivity
If multiple data labeling routines are used to increase productivity, then labeling throughput improves, but measurement precision deteriorates
Solution Approach 1:
The patent applies partial action by not requiring all samples to undergo all labeling routines. Instead, it uses cluster-based analysis to identify only those samples that need additional labeling review. This selective approach maintains high productivity while ensuring accuracy is improved where needed, rather than uniformly applying excessive labeling to all samples.
Solution Approach 2:
The system changes the parameter of sample selection from uniform/random to cluster-based prioritization. By analyzing cluster characteristics such as intra-cluster similarity and inter-cluster distance, the system dynamically determines which samples require additional labeling attention, thereby optimizing the balance between productivity and precision.
3Productivity
If conventional hybrid labeling is used to process more data, then productivity increases, but reliability deteriorates due to inconsistencies
Solution Approach 1:
The patent performs preliminary action by generating embedding clusters and analyzing their characteristics before finalizing labels. This pre-analysis allows the system to proactively identify potential inconsistency patterns in clusters, and preemptively direct those samples to additional labeling routines, thereby maintaining high reliability even when processing large volumes of data through multiple labeling approaches.
Data Source
AI summary
Systems and methods for identifying data labels for submitting to additional data labeling routines based on embedding clusters are described. For example, the system may retrieve an unlabeled dataset, wherein the unlabeled dataset comprises a plurality of unlabeled samples. The system may generate a plurality of embeddings based on the unlabeled dataset, wherein the plurality of embeddings comprises a respective embedding for each unlabeled sample in the plurality of unlabeled samples. The system may cluster the plurality of embeddings into a plurality of clusters. The system may, based on the first cluster, determine a first labeled sample of the first labeled dataset corresponding to the first cluster and determine to submit the first labeled sample to a second data labeling routine. The system may generate, using the second data labeling routine, a second labeled sample based on the first labeled sample.