Classifier Training with Noisy Label Correction
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Solution Overview
Problem
Existing methods for training classifiers face challenges in achieving effective classification results due to noisy labels in datasets, which are often incorrect and difficult to correct, especially when clean datasets are not readily available, leading to inefficiencies in classification accuracy.
Innovation Solution
A method that divides a sample dataset into sub-datasets, uses one as a test dataset and the others as a train dataset, calculates a ratio of mislabeled samples, and updates the classifier's loss function based on this ratio to alleviate noise impact, allowing for training completion without additional clean data or manual annotation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation is used to correct noisy labels, then classification accuracy is improved, but labor cost and time consumption increase significantly
Solution Approach 1:
The system performs self-diagnosis and self-correction by automatically identifying noisy samples through uncertainty measurement and correcting them via retraining, eliminating the need for manual annotation while maintaining classification accuracy
Solution Approach 2:
The system establishes a feedback loop where classification results are evaluated, uncertain samples are identified, corrections are applied, and the model is retrained, creating a continuous improvement cycle that automatically resolves labeling errors without human intervention
2Measurement precision
If noise detection algorithms are used to filter noisy samples, then classification accuracy is improved, but the methods are limited to specific noise distribution cases
Solution Approach 1:
The system dynamically adjusts the threshold for identifying noisy samples based on the uncertainty distribution of the dataset, allowing it to adapt to different noise levels and distributions without requiring predefined noise models or assumptions
Solution Approach 2:
The noise detection mechanism is dynamic rather than static, adjusting its sensitivity and thresholds based on the actual data characteristics and uncertainty measurements, enabling it to handle various noise distributions effectively
3Measurement precision
If a clean dataset is required for training, then classification accuracy is improved, but obtaining clean data creates a bottleneck in actual applications
Solution Approach 1:
The system converts the harmful effect of noisy labels into a beneficial process by using the uncertainty measurements from noisy data to automatically identify and correct errors, transforming the problem of having only noisy data into an opportunity for automatic quality improvement without requiring separate clean datasets
Solution Approach 2:
The system introduces uncertainty measurement as an intermediary mechanism that bridges the gap between noisy training data and reliable classification, enabling effective learning from imperfect data without requiring access to clean reference data
4Reliability
If constant checking and manual correction of noisy labels is performed, then label quality is improved, but a lot of manpower is required
Solution Approach 1:
The system automatically identifies and corrects labeling errors through uncertainty measurement and iterative retraining, performing self-quality control without requiring human annotators to review and correct each noisy sample
Solution Approach 2:
The system implements automated feedback loops where classification performance is continuously monitored, problematic samples are identified, and corrections are automatically applied through retraining, replacing manual quality control processes with autonomous systems
Data Source
AI summary
A data processing method and apparatus are disclosed. The method includes: obtaining a sample dataset, where each sample in the sample dataset includes a first label; dividing the sample dataset into K sample sub-datasets, determining a group of data from the K sample sub-datasets as a test dataset, and using sample sub-datasets other than the test dataset as a train dataset; training the classifier by using the train dataset, and classifying the test dataset by using a trained classifier, to obtain a second label of each sample in the test dataset; obtaining a first indicator and a first hyper-parameter at least based on the first label and the second label; obtaining a loss function of the classifier at least based on the first hyper-parameter, where the loss function is used to update the classifier; and completing training of the classifier when the first indicator meets a preset condition.