Neural Network Classifier with Posterior Transition Matrix for Noisy Labels
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Solution Overview
Problem
Neural networks trained on datasets with mislabeled data face classification errors due to instance-dependent label noise, where the underlying noise distribution does not follow a uniform distribution, making existing noise transition matrix assumptions invalid.
Innovation Solution
A posterior transition matrix (PTM) is used to minimize cross-entropy between noisy labels and classifier outputs, combined with an estimated noise transition matrix (NTM) to reduce estimation errors, improving the robustness of neural network training even with noisy labels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a noise transition matrix (NTM) is used to model label noise, then classification accuracy can be improved, but the assumption of uniform noise distribution across instances limits its effectiveness when noise is instance-dependent
Solution Approach 1:
The patent applies local quality by transitioning from a global noise transition matrix to instance-specific transition matrices. Each instance x has its own transition matrix T(x) that captures local noise characteristics, allowing the model to adapt to varying noise patterns across different data points rather than assuming uniform noise distribution.
Solution Approach 2:
The patent implements dynamics by making the transition matrix adaptive and instance-dependent rather than static and uniform. The transition probabilities are computed dynamically for each instance based on its specific characteristics, enabling the system to respond to varying noise conditions across different data points.
2Productivity
If traditional supervised learning is used with noisy labels, then training is simple and fast, but classification errors increase due to mislabeled data
Solution Approach 1:
The patent applies feedback by using the computed transition matrices to correct the loss function during training. The corrected loss incorporates information about label noise probabilities, allowing the model to learn from noisy labels while compensating for their inaccuracies through the feedback mechanism of the adjusted optimization objective.
Solution Approach 2:
The patent implements parameter changes by modifying the loss function parameters to account for noise. The corrected loss function uses the transition matrices to adjust the weights and probabilities assigned to different labels during training, transforming the standard supervised learning objective into a noise-robust optimization problem.
3Adaptability or versatility
If noise transition matrices are estimated from data, then the model can adapt to actual noise patterns, but estimation errors increase when there are insufficient known clean or mislabeled data points
Solution Approach 1:
The patent applies preliminary action by computing instance-specific transition matrices before the main training process. This preliminary computation of T(x) for each instance allows the model to incorporate noise awareness from the outset, rather than attempting to estimate noise characteristics during training when data may be insufficient.
Data Source
AI summary
An artificial intelligence (AI) classifier is trained using supervised training and an effect of noise in the training data is reduced. The training data includes observed noisy labels. A posterior transition matrix (PTM) is used to minimize, in a statistical sense, a cross entropy between a noisy label and a function of the classifier output. A loss function using the PTM is provided to use in training the classifier. The classifier provides final output predictions with good performance even with the existence of noisy labels. Also, information fusion is included in the classifier training using the PTM and an estimated noise transition matrix (NTM) to reduce estimation error at the classifier output.