Annotated Dataset Refinement Using Iterative Error Feedback
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Solution Overview
Problem
Existing machine-learning algorithms for autonomous systems require large amounts of high-quality annotated data, but manual correction of annotations is costly and error-prone, leading to reduced performance and delayed launches.
Innovation Solution
An iterative data-annotation refinement scheme where a machine-learning algorithm evaluates and refines annotations, allowing for automated and efficient correction of errors and missing annotations in datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual correction of annotations is performed to improve data quality, then annotation accuracy is improved, but cost and time consumption increase
Solution Approach 1:
The system uses the trained machine learning algorithm to automatically evaluate and identify annotation errors in the dataset, enabling self-correction without manual intervention. The algorithm processes images, compares predictions with annotations, and generates erroneous datasets for re-annotation, creating a self-service annotation quality improvement system.
Solution Approach 2:
The system implements a feedback loop where the trained algorithm evaluates annotations, identifies errors, and feeds this information back for re-annotation. This iterative feedback process continuously improves annotation quality by using the algorithm's predictions to guide correction efforts, reducing the need for extensive manual review.
2Manufacturing precision
If manual correction of annotations is performed to improve data quality, then annotation accuracy is improved, but costs increase
Solution Approach 1:
The system automates the annotation quality improvement process by using the trained algorithm to self-evaluate and identify errors, eliminating the need for expensive manual correction for every annotation. This self-service approach significantly reduces the labor costs associated with annotation verification and correction.
Solution Approach 2:
The trained machine learning algorithm acts as an intermediary between the annotated dataset and the final quality assurance. Instead of direct manual correction of all annotations, the algorithm mediates by identifying specific erroneous cases that require human attention, reducing the overall cost while maintaining quality.
3Manufacturing precision
If iterative refinement is performed to improve annotation quality, then data quality is improved, but processing time increases
Solution Approach 1:
The system does not perform exhaustive manual correction of all annotations but instead applies partial action by using the algorithm to identify and focus only on erroneous cases. This selective approach refines data quality efficiently by processing only the necessary portions of the dataset that contain errors, rather than uniformly processing the entire dataset.
Solution Approach 2:
The iterative refinement process maintains continuous useful action by automatically cycling through evaluation, error identification, and re-annotation without interruption. The system continuously processes datasets through multiple iterations, with each iteration building on previous improvements, maintaining steady progress toward quality enhancement without idle periods.
4Productivity
If automated evaluation using machine learning is used to reduce manual work, then productivity is improved, but measurement precision of annotation errors may worsen
Solution Approach 1:
The system uses feedback from the trained algorithm's predictions to continuously improve error detection. By comparing algorithm predictions with existing annotations and iteratively refining based on identified errors, the system enhances its measurement precision over time, allowing automated evaluation to maintain both productivity and accuracy.
Solution Approach 2:
The system performs preliminary action by training the machine learning algorithm on initially annotated data before using it for evaluation. This preliminary training phase enables the algorithm to develop accurate error detection capabilities, ensuring that subsequent automated evaluation maintains high measurement precision while improving productivity.
Data Source
AI summary
The present invention relates to a method for improving annotated datasets for training machine-learning algorithms. More specifically, the present invention relates to a method employing an iterative scheme where a machine-learning algorithm is used trained on an annotated dataset, and subsequently used to evaluate the annotated dataset by feeding the annotated dataset into the machine-learning algorithm in order to extract an erroneous dataset from the input dataset, where the erroneous dataset includes images associated with indications of annotation errors and/or missing annotations in the annotated dataset. Then, the erroneous dataset is re-annotated, and the training and evaluation steps are repeated until the number of images of the annotated dataset with annotation errors and/or missing annotations is below a threshold.