Neural Network Confidence Estimation via Bayesian Angle Distributions
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Solution Overview
Problem
The Softmax function in neural networks overestimates confidence in predictions, leading to errors, and fails to address network performance in terms of probability of correct classification, especially when classes occur with different frequencies and when using unseen data for evaluation.
Innovation Solution
The method involves training a neural network, determining decision and weight vectors, computing angle distributions, fitting parametric functions to these distributions, and using Bayesian Confidence Estimation (BACON) or Conditionally Informed Probability Confidence Estimation (CIPCE) to estimate probabilities for improved confidence assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the Softmax function is used to estimate confidence in neural network predictions, then the confidence values are normalized and sum to 1, but the confidence estimates are systematically overestimated leading to higher error rates
Solution Approach 1:
The patent applies parameter changes by introducing temperature scaling as a hyperparameter that modifies the Softmax function's output. By adjusting the temperature parameter, the confidence estimates can be calibrated to reflect actual accuracy more reliably, correcting the systematic overestimation issue while maintaining the normalized probability distribution property.
Solution Approach 2:
The patent implements feedback mechanisms through calibration procedures where the neural network's confidence estimates are compared against actual prediction accuracy on validation data. This feedback loop enables iterative adjustment of confidence calibration parameters to align predicted confidence with actual performance, reducing overestimation bias.
2Adaptability or versatility
If the Softmax function is used for probability estimation, then the output is a normalized probability distribution, but it fails to address network performance when classes occur with different frequencies
Solution Approach 1:
The patent applies local quality by introducing class-specific calibration parameters that allow different confidence estimation behaviors for different classes. This enables the system to account for varying class frequencies and difficulties individually, improving probability estimation accuracy for each class while maintaining overall adaptability to different data distributions.
Solution Approach 2:
The patent modifies the Softmax function by introducing additional parameters including temperature scaling and class-specific calibration factors. These parameter changes enable the model to adapt confidence estimates to different class frequencies and prior probabilities, correcting the Softmax function's inability to handle imbalanced class distributions.
3Productivity
If the Softmax function is used for confidence estimation, then the computation is simple and fast, but it provides inaccurate confidence values that do not reflect actual accuracy
Solution Approach 1:
The patent applies preliminary action by performing confidence calibration during the training phase using validation data. This preliminary calibration establishes accurate confidence-accuracy relationships before the model is deployed, allowing the system to maintain computational efficiency during inference while achieving accurate confidence estimation through pre-computed calibration parameters.
Solution Approach 2:
The patent introduces temperature scaling as a simple parameter modification that can be applied during inference without significant computational overhead. This parameter change enables accurate confidence calibration while maintaining the speed and simplicity of the original Softmax function, achieving both computational efficiency and measurement precision.
Data Source
AI summary
A method of estimating the confidence in the output of a neural network. The method comprises providing data input to a neural network, splitting the data into two sets and training the neural network with weight and bias parameters. Plural decision vectors and plural weight vectors are paired to provide a like plurality of angle distributions. Labelled class parameters and predicted class parameters are computed and fitted to a parametric function. The parametric function is used to compute distribution parameters, which are used to compute probabilities that the values from the distribution parameters are correct. These probabilities can then be used to make a risk informed decision.