Confidence score - True Class Probability (TCP)
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Description
True Class Probability (TCP)[Corbiere et al., 2021] is a confidence score method that aims to flag whether a neural network prediction is conform to the true class probability distribution. It is a module that can be plugged on a neural network, and only requires the output of a given layer for training and operational phase. Specifically, TCP requires access to
the penultimate layer, which outputs the probability distributions (the logits).Training TCP boils down to a classification problem, which requires to have a labelled dataset (although it only requires the original labels; no additional labelling effort is required).
It is dedicated to classification and detection use cases.
Documentation
Methodological Guidelines
- Methodological guideline for Robustness Functional Set
- Methodological Guideline and Evaluation tools for robust artificial intelligence
Benchmark
State of the art
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Support for True Class Probability must be obtained by sending an email to support@confiance.ai
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- Functional maturity
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