Noise Model for Robust ML Training with Label Noise
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Solution Overview
Problem
Machine learning systems face challenges in robustness due to label noise in training datasets, which affects the performance of classification models, especially when high-quality annotated data is costly and time-consuming to obtain.
Innovation Solution
The implementation of a machine learning system that models label noise explicitly by using a prior distribution to model stochastic label flips and an approximating posterior distribution to generate posterior probabilities, allowing for the training of recognition and posterior systems to improve classification accuracy in noisy environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional training methods are used on noisy labeled data, then the model can be trained quickly and with less resource investment, but the classification performance and accuracy deteriorate due to label noise
Solution Approach 1:
The patent introduces an intermediary component - a noise model - that mediates between the noisy labeled data and the classifier. This noise model characterizes the labeling process and allows the system to account for label noise during training, thereby maintaining both training efficiency and classification performance. The noise model acts as a bridge that transforms the problematic noisy data into useful training signals.
Solution Approach 2:
The patent modifies the training objective by introducing parameters that characterize the noise process (noise probabilities, confusion matrices). By changing the parameters of the training objective function to include noise modeling components, the system can optimize for both speed and accuracy simultaneously, rather than having to trade one off against the other.
2Measurement precision
If high-quality annotated data is obtained through extensive labeling processes, then the classification accuracy improves, but the time and resource costs increase significantly
Solution Approach 1:
The patent converts the harmful effect of label noise into a beneficial opportunity. Instead of discarding noisy data or investing heavily in cleaning it, the system models the noise process itself and uses this model to improve training. The noise, rather than being a obstacle, becomes a source of information about the data distribution and labeling process, enabling efficient training with lower-quality data.
Solution Approach 2:
The system performs self-correction by automatically modeling and compensating for label noise without requiring external intervention or manual data cleaning. The noise model learns from the data itself and automatically adjusts the training process to account for labeling errors, eliminating the need for time-consuming manual annotation or verification processes.
3Reliability
If data cleaning processes are applied to remove noisy labels, then the training data quality improves, but the processing time and computational resources increase
Solution Approach 1:
The patent extracts the noise characterization from the data cleaning process. Instead of removing noisy samples through complex filtering and validation procedures, the system extracts a noise model that captures the essential characteristics of labeling errors. This extracted model is then used during training to compensate for noise, avoiding the need for complex pre-processing while maintaining data quality.
Data Source
AI summary
Machine learning classification models which are robust against label noise are provided. Noise may be modelled explicitly by modelling “label flips”, where incorrect binary labels are “flipped” relative to their ground truth value. Distributions of label flips may be modelled as prior and posterior distributions in a flexible architecture for machine learning systems. An arbitrary classification model may be provided within the system. The classification model is made more robust to label noise by operation of the prior and posterior distributions. Particular prior and approximating posterior distributions are disclosed.