On-the-Fly Image Classifier Calibration With Adaptive Prediction Smoothing
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Solution Overview
Problem
Existing image classifiers often suffer from miscalibration, particularly in high-assurance settings, leading to overconfidence in their predictions, which can result in critical errors, such as misclassifying a stop sign as a no-speed-limit sign, endangering lives in self-driving cars.
Innovation Solution
An on-the-fly calibration technique called Label Smoothing from Predictions (LSP) uses soft labels based on the image classifier's own predictions to update training signals, adapting the smoothing factor dynamically and accounting for relative errors between target and non-target classes, thereby improving likelihood estimates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional label smoothing is applied during training, then overconfidence is reduced, but likelihood estimates remain inaccurate
Solution Approach 1:
The patent applies dynamics by making the smoothing factor adaptive rather than static. The smoothing parameter is dynamically adjusted based on the classifier's current performance and confidence levels during training, allowing the system to respond to changing conditions and achieve better calibration without sacrificing likelihood estimate accuracy
Solution Approach 2:
The patent changes the parameter of the smoothing factor from a fixed value to a dynamically adjusted parameter. By modifying how the smoothing parameter is determined (based on predicted probabilities and confidence thresholds), the system resolves the contradiction between reducing overconfidence and maintaining accurate likelihood estimates
2Measurement precision
If one-hot encoding labels are used for training, then classification accuracy is maintained, but overconfidence in predictions occurs
Solution Approach 1:
The patent applies local quality by differentiating the treatment of different classes in the label vector. Instead of uniform one-hot encoding, the system applies localized smoothing to specific classes based on the classifier's predicted probabilities, creating non-uniform label vectors that preserve accuracy while improving confidence calibration for specific classes
3Reliability
If the smoothing factor is increased to reduce overconfidence, then calibration improves, but accuracy on unseen data deteriorates
Solution Approach 1:
The patent uses dynamics by implementing a adaptive smoothing factor that changes during training based on the classifier's performance. The smoothing parameter is increased when overconfidence is detected and decreased when accuracy is at risk, allowing the system to navigate the trade-off between calibration quality and generalization accuracy
Solution Approach 2:
The patent implements feedback by using the classifier's own predictions and confidence levels to adjust the smoothing factor. The system continuously monitors prediction confidence and accuracy, then feeds this information back to modify the smoothing parameter, creating a closed-loop control that balances calibration and generalization
Data Source
AI summary
A method of training an image classifier. The image classifier determines a prediction vector of class probabilities for an input image. First, one or more initial training steps are performed, resulting in current values for the trainable parameters of the image classifier. A training image and corresponding class label are selected, and a current label vector is determined for training the image classifier on the training image. To this end, the image classifier is applied to the training image according to the current parameter values. If the class predicted by the image classifier is equal to the class label, the current label vector is determined as a soft label based on the prediction vector. A current training step of training the image classifier on the training image is performed using the current label vector.