Mislabel Detection Using Multi-Fold Model Validation
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Solution Overview
Problem
Mislabeled training data in machine learning models leads to inaccurate algorithms, particularly in defect detection tasks, due to flawed training data such as mislabeled examples, missing labels, or imprecise labels, which results in overfitting and reduced model performance.
Innovation Solution
A method and system for automatically detecting mislabeled samples in a labeled dataset using multi-fold training, where the dataset is divided into subsets to train multiple machine-learning models, generate prediction labels with confidence scores, and compare these with ground-truth labels to identify and correct mislabels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual inspection methods are used to detect defects, then human inspectors can apply their expertise to identify defects, but the process consumes significant time and resources and produces inconsistent results
Solution Approach 1:
The patent replaces manual mechanical inspection with an automated machine learning-based inspection system. The system uses trained models to analyze product images and automatically detect defects, eliminating the need for human inspectors to manually examine each product. This substitution maintains high inspection consistency through algorithmic determinism while dramatically reducing inspection time and resource consumption.
2Productivity
If machine learning models are trained with flawed training data containing mislabels, then the models can be developed quickly, but the resulting algorithms become inaccurate and fail to achieve expected performance
Solution Approach 1:
The patent applies preliminary action by implementing a pre-training validation process that checks training data quality before full model training begins. The system performs initial training runs, validates model predictions against known good samples, and iteratively refines the training data by identifying and correcting mislabeled examples. This preliminary validation ensures data quality is addressed before committing to extensive training cycles, preventing wasted computational resources on flawed datasets.
3Measurement precision
If extensive manual review of training data is performed to correct mislabels, then data quality improves and model accuracy increases, but the time and resources required for data preparation increase significantly
Solution Approach 1:
The patent implements self-service by creating an automated system that identifies and corrects its own training data errors. The machine learning model generates predictions on training samples, and the system automatically compares these predictions with existing labels. When discrepancies are found, the system flags potential mislabels for review or automatically corrects them based on confidence thresholds. This self-correcting mechanism eliminates the need for extensive manual data review while maintaining high label accuracy.
4Device complexity
If traditional supervised learning approaches are used without data validation, then the training process is simple and fast, but the models suffer from overfitting and reduced performance on defect detection tasks
Solution Approach 1:
The patent implements feedback mechanisms by continuously monitoring model performance during training and using this information to adjust the training process. The system tracks validation metrics, identifies performance degradation patterns that indicate overfitting, and automatically responds by adjusting learning rates, adding regularization, or retrieving additional diverse training samples. This closed-loop feedback system maintains model reliability without requiring complex manual intervention or significantly increasing training complexity.
Data Source
AI summary
A system or method for identifying mislabels in a training dataset. The labeled dataset includes multiple labeled samples, each of which is labeled with a ground-truth label. The labeled samples are divided into multiple training subset and hold-out test subset. For each training subset and corresponding hold-out test subset, a machine learning model is trained using the training subset, and the trained machine learning model is applied to the hold-out test subset to generate prediction labels for samples in the corresponding hold-out test subset. Each prediction label has a confidence score indicating a likelihood of the prediction label is correct. A pair of prediction label and ground truth label corresponding to a same sample is compared to determine whether there is a candidate mislabel. The system determines whether the candidate mislabel is a mislabel based in part on a confidence score of the prediction label.