Class-wise Loss Scaling for Neural Network Calibration
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Solution Overview
Problem
Existing deep learning models face challenges in achieving accurate calibration of prediction results due to performance degradation and biased information learning, leading to varying confidence levels across different classes.
Innovation Solution
A method and apparatus for controlling a loss scale by class, which involves identifying class-wise losses in a neural network, normalizing these losses to analyze relative correlations, estimating loss scaling weights, and calculating a weighted sum of class-wise losses to derive a total loss, thereby improving the accuracy of calibrated prediction results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing calibration methods are applied to improve prediction confidence calibration, then calibration accuracy is improved, but performance degradation occurs due to additional calculations during model training
Solution Approach 1:
The patent changes the parameter of loss calculation by introducing class-wise loss scaling weights that are dynamically adjusted based on calibration performance. Instead of uniform loss treatment, each class's loss is scaled by a specific weight parameter that is optimized to improve calibration while maintaining training efficiency.
Solution Approach 2:
The patent segments the overall loss function into class-wise components, allowing differential treatment of each class's contribution to the total loss. This segmentation enables targeted calibration improvement for specific classes without uniformly impacting all classes' training dynamics.
2Measurement precision
If existing calibration methods are applied to obtain accurately calibrated prediction results, then calibration quality is improved, but additional calibration costs are incurred
Solution Approach 1:
The patent merges the calibration objective with the training objective by integrating calibration loss into the overall training loss function. This combination allows the model to learn both predictive accuracy and calibration quality simultaneously during a single training process, eliminating separate calibration steps and reducing overall costs.
Solution Approach 2:
The patent performs calibration learning during the model training phase itself, rather than as a subsequent separate step. By incorporating calibration objectives into the training process from the beginning, the model acquires calibration capabilities as part of its fundamental learning, avoiding additional post-training calibration costs.
3Reliability
If deep learning models learn from biased information in data, then model captures real-world patterns, but confidence calibration varies across classes leading to inaccurate overall calibration
Solution Approach 1:
The patent applies local quality by treating each class differently in the loss function through class-specific scaling weights. Instead of applying a uniform calibration approach to all classes, the method adjusts the loss contribution of each class individually, allowing the model to account for class-specific calibration issues while maintaining the ability to learn from biased real-world data distributions.
Data Source
AI summary
A method for controlling a loss scale by class includes identifying class-wise losses of a neural network, analyzing a relative correlation by normalizing the identified class-wise losses, estimating class-wise loss scaling weights according to a result of the analyzing of the relative correlation, and calculating a weighted sum of the class-wise losses by applying the estimated class-wise loss scaling weights to the identified class-wise losses of the neural network, and deriving a total loss of the neural network by summing the identified class-wise losses and the weighted sum of the class-wise losses.