Video Data Relabeling Using Reviewer Feedback Scores
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Solution Overview
Problem
Current techniques for labeling data for machine learning models are time-consuming, resource-intensive, prone to errors, and result in inaccurate labels, leading to the generation of erroneous models and outputs.
Innovation Solution
A video system that determines when to relabel data by calculating various scores based on reviewer expertise, user feedback, and intrinsic label information to identify mislabeled data, corrects the labels, and retrains the model with the corrected data, thereby conserving computing and networking resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual labeling techniques are used for machine learning data, then labeling can be performed with simple processes, but the labeling is time-consuming and resource-intensive
Solution Approach 1:
The system enables automated self-labeling through machine learning models that can independently assign labels to data without extensive manual intervention. The model processes data, generates predictions, and creates labels automatically, reducing dependency on manual labeling while maintaining simplicity in the overall process.
Solution Approach 2:
Manual labeling operations are replaced with automated machine learning-based labeling systems. The mechanical process of human reviewers examining and labeling data is substituted with computational algorithms that process data efficiently, thereby improving productivity while keeping the process accessible.
2Ease of manufacture
If manual labeling is performed by human specialists, then labels can be generated, but the process is prone to errors and inaccuracies
Solution Approach 1:
The system implements feedback mechanisms where model predictions are evaluated against ground truth data when available, and labeling accuracy is continuously monitored. Reviewer feedback and model performance metrics are used to refine labeling processes, reducing errors and improving label accuracy over time through iterative improvement.
Solution Approach 2:
The machine learning model performs preliminary labeling actions before final label assignment. The model generates initial labels that can be reviewed and corrected, providing a preliminary filter that reduces the burden on human specialists and minimizes errors by having the automated system handle routine labeling tasks with high accuracy.
3Reliability
If extensive labeling is performed to improve model accuracy, then model performance improves, but computing and networking resources are consumed
Solution Approach 1:
The system applies partial labeling strategies where only the most critical or uncertain data points require extensive manual review, while other data points are labeled automatically with sufficient accuracy. This selective approach maintains model accuracy by focusing resources on cases that truly need human intervention rather than uniformly labeling all data.
Solution Approach 2:
The system dynamically adjusts labeling parameters such as confidence thresholds, review priorities, and resource allocation based on data characteristics and model performance needs. By changing these parameters, the system optimizes the balance between achieving sufficient label quality for model accuracy and minimizing resource consumption during the labeling process.
Data Source
AI summary
A device may receive video data identifying videos, and may process the video data with a machine learning model, to determine classifications. The device may generate labels for the videos, and may calculate event severity scores and event severity labels. The device may calculate event severity incoherence scores, and may calculate user feedback scores of users associated with the device. The device may determine reviewer mistrust scores, and may calculate time review scores. The device may calculate reviewer bias scores, and may determine relabeling scores for the videos based on the event severity incoherence scores, the user feedback scores, the reviewer mistrust scores, the time review scores, and the reviewer bias scores. The device may generate new labels for one or more of the videos based on the relabeling scores, and may retrain the machine learning model, with the new labels, to generate a retrained machine learning model.