Post-processing model for classifier output calibration
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Solution Overview
Problem
Existing AI systems for decision-making in dynamically changing environments struggle to provide consistently well-calibrated predictive uncertainties, especially under dataset shifts, as previous methods fail to maintain accurate confidence scores when the input distribution drifts.
Innovation Solution
A computer-implemented method for post-processing classifier output data involves generating perturbed sample pairs using methods like Fast Gradient Signed Method, determining a post-processing model based on these pairs, and applying it to transform unnormalized logits into well-calibrated outputs, ensuring reliability across domain shifts without requiring retraining of the classifier.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional classification models are used, then in-domain prediction accuracy is achieved, but predictive uncertainty calibration deteriorates under dataset shifts
Solution Approach 1:
The method performs preliminary actions by generating perturbed validation samples and training a post-processing model before actual deployment. This advance preparation enables the system to handle domain shifts effectively when encountered in production, as the post-processing model is already trained on diverse perturbed data representing potential distribution shifts.
Solution Approach 2:
A post-processing model is introduced as an intermediary component between the original classifier and the final prediction output. This intermediate layer transforms the classifier's raw predictions into calibrated uncertainty estimates, decoupling the classification accuracy function from the uncertainty calibration function and allowing each to be optimized independently.
2Reliability
If probabilistic neural networks are trained for uncertainty awareness, then in-domain calibration is achieved, but computational complexity increases
Solution Approach 1:
The overall prediction system is segmented into two independent components: the original classifier for accuracy and a separate post-processing model for uncertainty calibration. This segmentation allows each component to be optimized for its specific function without the complexity of integrating uncertainty modeling into the base classifier architecture.
Solution Approach 2:
The post-processing model uses simple, computationally inexpensive transformations (such as temperature scaling or Platt scaling) applied to the classifier's output logits. These lightweight post-processing steps provide calibration without requiring complex probabilistic neural network architectures or extensive retraining of the base model.
3Measurement precision
If post-processing methods are applied to unnormalized logits, then in-domain calibration is improved, but robustness to domain drift is reduced
Solution Approach 1:
The method dynamically adapts to distribution shifts by training the post-processing model on validation samples that have been perturbed to represent various domain shift scenarios. This dynamic training approach enables the post-processing model to adjust confidence scores appropriately across different data distributions, rather than being fixed for in-domain conditions only.
Solution Approach 2:
The method changes the training parameters of the post-processing model by using perturbed validation samples with modified data distributions. By exposing the model to various perturbation levels and types during training, it learns to maintain calibration accuracy across different distributional conditions, effectively adapting to parameter changes in the input data.
Data Source
Figure 1
AI summary
The invention is directed to a computer-implemented method for post-processing output data of a classifier, comprising the steps: a. Providing a validation data set with a plurality of labelled sample pairs, wherein each labelled sample pair comprises a model input and a corresponding model output (S1) ; b. Providing a plurality of perturbation levels (S2); c. Generating at least one perturbated sample pair for each labelled sample pair of the plurality of labelled sample pairs using a perturbation method based on the respective labelled sample pair and at least one perturbation level of the plurality of perturbation levels (S3); d. Determining a post-processing model based on the plurality of perturbated sample pairs (S4); e. Applying the determined post-processing model on testing data to post-process the output data of the classifier (S5); and f. Providing the post-processed output data of the classifier (S6). Further, the invention relates to a corresponding technical unit and computer program product.