Hierarchical Data Annotation System for AI Training
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Solution Overview
Problem
Current annotation methods for machine learning datasets are inefficient, leading to error propagation and difficulty in correcting initial annotation errors, especially in hierarchical annotation systems, which can delay industrial-scale deployment due to resource-intensive corrections and inconsistencies.
Innovation Solution
A method that utilizes a database to store and manage data facets, allowing for hierarchical data selection and filtering, decoupling the notion of machine learning models from datasets, and enabling dynamic construction of datasets based on annotation tasks, thereby allowing for efficient annotation and error correction without creating new subsets of data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data are annotated in a hierarchical way with multiple levels, then the AI can recognize multiple concepts (classification, detection, OCR), but error propagation occurs and corrections become difficult
Solution Approach 1:
The annotation system is segmented into independent hierarchical levels (classification, detection, OCR) that can be processed and corrected separately. Each level operates on the output of the previous level, allowing errors to be isolated and corrected at specific levels without affecting the entire annotation structure.
Solution Approach 2:
The system implements feedback mechanisms where annotation results from one level are fed back to verify and correct annotations at previous levels. This allows error detection and correction to propagate upward through the hierarchy, ensuring that corrections at any level can be validated against subsequent levels.
2Ease of operation
If multiple datasets are created for hierarchical annotation levels, then each level can be annotated independently, but the process becomes resource-intensive and time-consuming
Solution Approach 1:
Annotations at higher levels (classification, detection) are performed first as preliminary actions that guide and constrain subsequent annotation levels. This preliminary structuring reduces the search space and complexity for lower-level annotations, significantly reducing overall annotation time while maintaining independence of each level.
Solution Approach 2:
The annotation system uses a nested structure where lower-level annotations (detection, OCR) are contained within and dependent on higher-level annotations (classification). This nesting allows the system to reuse annotation results across levels, eliminating redundant work and reducing total annotation time while preserving operational independence.
3Manufacturing precision
If traditional annotation methods are used, then datasets can be annotated, but correcting initial annotation errors requires resource-intensive reannotation
Solution Approach 1:
The system implements feedback loops where annotation results are automatically verified against constraints from multiple levels. When errors are detected, the feedback mechanism identifies the specific level and location of errors, enabling targeted corrections rather than complete reannotation. This maintains high annotation accuracy while significantly reducing correction effort.
Solution Approach 2:
The system introduces intermediary verification layers between annotation levels that automatically check for consistency and errors. These intermediaries act as mediators that can identify and flag errors before they propagate, allowing for efficient correction without requiring resource-intensive manual reannotation of entire datasets.
4Adaptability or versatility
If hierarchical annotation is implemented, then multiple concepts can be recognized, but the complexity of managing multiple datasets increases
Solution Approach 1:
The system merges multiple hierarchical annotation levels into a unified data structure that is managed as a single integrated entity. This unified structure maintains the independence of each annotation level while providing centralized management, reducing complexity compared to managing separate datasets for each level. The system allows AI models to access and utilize annotations from all levels simultaneously.
Data Source
AI summary
The invention relates to a method of annotating training data for an artificial intelligence comprising the following steps:storing, in a database, a set of data to be annotated,storing, in said database, at least a first description of a first facet for data selection in said set of data, said first description being associated with a first task to be performed by said artificial intelligence,selecting said first facet in said database,applying said first facet to data in said set of data to obtain first filtered data,receiving at least a first annotation of said first filtered data, andstore said first annotation in the database in association with said first facet.