Label Propagation via Supervised Semantic Embedding Projections
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Solution Overview
Problem
Artificial intelligence implementations face challenges due to the scarcity and variability of labeled data, requiring specialized knowledge and resources, which hinders practical applications.
Innovation Solution
The system uses a supervised projection of semantic embeddings to propagate labels through sparsely labeled datasets, improves label quality with consistency scores, and enhances training efficiency using entropy scores, thereby reducing labeling costs and resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If labeled data is obtained through manual annotation, then label quality is improved, but time consumption and cost increase
Solution Approach 1:
The system performs preliminary embedding of all data into semantic space before labeling, creating a structured representation that enables efficient label propagation. This preliminary structural organization allows subsequent automatic labeling to achieve high quality without manual annotation, resolving the contradiction between label quality and time consumption
Solution Approach 2:
The system copies label information from labeled samples to unlabeled samples through label propagation in the embedded semantic space. By copying labels based on semantic similarity rather than manual annotation, the system maintains high label quality while dramatically reducing time consumption
2Measurement precision
If specialized knowledge is used for data labeling, then label accuracy is improved, but resource requirements and complexity increase
Solution Approach 1:
The system performs self-service by automatically propagating labels through the embedded semantic space without requiring specialized human annotators. The embedding model and label propagation algorithm work autonomously to maintain high label accuracy while reducing system complexity and resource requirements
Solution Approach 2:
The semantic embedding serves as an intermediary between raw data and labels. This intermediate representation captures semantic relationships that enable accurate label propagation without requiring specialized knowledge, thus maintaining label accuracy while reducing system complexity
3Reliability
If large amounts of labeled data are collected, then model performance is improved, but data acquisition cost and time increase
Solution Approach 1:
The system copies labels from the small set of manually labeled samples to the large set of unlabeled samples through label propagation in semantic space. This copying mechanism enables the system to utilize large amounts of data for training while avoiding the time-consuming process of manually labeling all samples, thus improving model performance without proportionally increasing data acquisition time
Solution Approach 2:
The system performs preliminary embedding and label propagation before model training, transforming a small labeled dataset into a large labeled dataset automatically. This preliminary expansion of labeled data enables subsequent model training to achieve high performance without the need to manually collect and label large amounts of data, reducing data acquisition time
Data Source
AI summary
Systems and methods for propagating labels through a sparsely labeled dataset using a supervised projection of a semantic embedding. The system may receive the dataset and a labeling task. It may determine a semantic graph by embedding the dataset using a language model. It may process the semantic graph in an artificial intelligence model, as well as determine: an optimal set of dimensions for the labeling task based on the semantic graph; a projected graph based on the optimal set of dimensions; a first distance between a first projection and a second projection in the projected graph; a first likelihood the first projection has a first label; and a second likelihood the first projection has a second label based in part on the first distance to the second projection. It may compare the first likelihood to the second likelihood and generate a recommendation to stop the labeling task.