Distance-Based Confidence Model for Neural Network Calibration
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Solution Overview
Problem
Conventional machine learning models, particularly deep neural networks, face challenges in accurately calibrating confidence levels for predictions, leading to poorly calibrated models that can result in costly wrong decisions.
Innovation Solution
A distance-based Learning from Errors (DBLE) framework that jointly trains a classification model and a confidence model using a distance-based representation space, where the classification model learns to minimize intra-class distances and maximize inter-class distances, and the confidence model estimates confidence scores by sampling new query encodings close to ground-truth centroids, thereby improving calibration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional machine learning models use softmax output as confidence values, then the model can generate predictions efficiently, but the confidence calibration is poor and confidence values do not reflect actual prediction accuracy
Solution Approach 1:
The patent segments the confidence estimation task from the classification task by introducing a separate confidence model that independently processes query encodings to generate confidence scores. This separation allows the classification model to focus on accurate predictions while the confidence model专门 estimates confidence calibration, resolving the contradiction between efficient prediction and accurate confidence measurement.
Solution Approach 2:
The patent introduces a distance-based representation space as an intermediary between the input data and confidence output. By transforming inputs into this specialized representation space and measuring distances to ground-truth centroids, the system achieves accurate confidence calibration without compromising prediction efficiency, as the intermediary handles the complex calibration computations.
2Reliability
If the classification model is trained to minimize classification errors, then prediction accuracy improves, but the model cannot provide reliable confidence estimations for misclassified samples
Solution Approach 1:
The patent performs preliminary action by training the confidence model specifically on misclassified samples from the training set. This preliminary training on error cases ensures that when the model encounters misclassified samples during inference, it has pre-learned appropriate confidence calibration strategies for these difficult cases, preventing loss of confidence information.
Solution Approach 2:
The patent changes the training parameters and objectives of the confidence model to focus specifically on misclassified samples rather than all samples. By adjusting the training data distribution and loss function to target error cases, the model learns to provide reliable confidence estimations specifically for misclassified samples while maintaining overall prediction accuracy.
3Measurement precision
If a distance-based representation space is introduced to improve confidence calibration, then confidence estimation accuracy improves, but the model complexity increases
Solution Approach 1:
The patent makes the distance-based representation space and confidence model universal by designing them to work with any classification model output. The confidence model accepts query encodings from the classification model and ground-truth labels as input, and can be applied across different classification tasks and datasets, reducing overall system complexity through reusability rather than requiring task-specific complex architectures.
Data Source
AI summary
A method includes receiving a training data set including a plurality of training data subsets. From two or more training data subsets in the training data set, the method includes selecting a support set of training examples and a query set of training examples. The method includes determining, using the classification model, a centroid value for each respective class. For each training example in the query set of training examples, the method includes generating, using the classification model, a query encoding, determining a class distance measure, determining a ground-truth distance, and updating parameters of the classification model. For each training example in the query set of training examples identified as being misclassified, the method further includes generating a standard deviation value, sampling a new query, and updating parameters of the confidence model based on the new query encoding.