Entropy-Based Logit Transformation for Neural Network Confidence Calibration
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Deep neural networks (DNNs) often suffer from poor calibration properties, leading to overconfidence and limited feasibility in real-world applications, particularly in safety-critical and high-stake domains like autonomous vehicles and medical diagnostics, where reliable confidence measures are essential for decision-making.
Innovation Solution
An entropy-based calibration function, also known as a logit transformation function, is introduced to calibrate confidence scores of classification networks by performing a per-datum logit transformation, which is a learnable function with two parameters optimized using negative log likelihood minimization, integrated into the output layer of DNNs to produce more reliable confidence scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If DNNs are trained to achieve high accuracy, then classification performance is improved, but confidence calibration deteriorates leading to overconfidence
Solution Approach 1:
The calibration function is trained in advance on a calibration dataset before deployment. This preliminary calibration step adjusts the confidence scores output by the DNN to better reflect true accuracy, preventing overconfidence issues when the model is deployed for actual classification tasks.
Solution Approach 2:
The calibration function introduces learnable parameters that transform the DNN's output confidence scores. By optimizing these parameters on calibration data, the system adjusts the confidence scale to achieve better alignment between predicted confidence and actual accuracy without retraining the entire DNN.
2Reliability
If confidence scores are adjusted to improve calibration, then reliability is improved, but model performance may deteriorate
Solution Approach 1:
The system separates the classification function from the confidence calibration function. The DNN handles classification accuracy while a distinct calibration function handles confidence score adjustment. This segmentation allows independent optimization of each function, preventing degradation of classification performance while improving confidence calibration.
Solution Approach 2:
The calibration function acts as an intermediary layer between the DNN's raw output and the final confidence score. It transforms the DNN's predictions without altering the underlying classification logic, serving as a mediator that adjusts confidence levels while preserving the original model's classification accuracy.
3Reliability
If a calibration function is introduced to improve confidence scores, then reliability is improved, but device complexity increases
Solution Approach 1:
The calibration function is a lightweight, simple model that can be trained once on calibration data and then applied repeatedly without requiring complex computational resources. It acts as a simple transformation layer that adds minimal complexity compared to retraining the entire DNN.
Solution Approach 2:
The calibration function uses a small number of learnable parameters that are optimized on calibration data. This parameter-based approach is much simpler than structural modifications to the DNN, requiring minimal additional complexity while achieving effective confidence calibration.
Data Source
AI summary
Disclosed is a technical solution to calibrate confidence scores of classification networks. A classification network has been trained to receive an input and output a label of the input that indicates a class of the input. The classification network also outputs a confidence score of the label, which indicates a likelihood of the input falling into the class, i.e., a confidence level of the classification network that the label is correct. To calibrate the confidence of the classification network, a logit transformation function may be added into the classification network. The logic transformation function may be an entropy-based function and have learnable parameters, which may be trained by inputting calibration samples into the classification network and optimizing a negative log likelihood based on the labels generated by the classification network and ground-truth labels of the calibration samples. The trained logic transformation function can be used to compute reliable confidence scores.