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Classifier calibration

Confidence calibration – the problem of predicting probability estimates representative of the true correctness likelihood – is important for classification models in many applications. The two common calibration metrics are Expected Calibration Error (ECE) and Maximum Calibration Error (MCE).

Papers

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Showing 1–29 of 29 papers

TitleStatusHype
PrePrompt: Predictive prompting for class incremental learningCode1
Multivariate Confidence Calibration for Object DetectionCode1
Multi-class probabilistic classification using inductive and cross Venn-Abers predictorsCode1
FedFA: Federated Learning with Feature Anchors to Align Features and Classifiers for Heterogeneous DataCode1
No Fear of Classifier Biases: Neural Collapse Inspired Federated Learning with Synthetic and Fixed ClassifierCode1
Generalized and Incremental Few-Shot Learning by Explicit Learning and Calibration without ForgettingCode1
Masksembles for Uncertainty EstimationCode1
Danish Fungi 2020 -- Not Just Another Image Recognition DatasetCode1
How Well Do Self-Supervised Models Transfer?Code1
High Frequency Residual Learning for Multi-Scale Image Classification—0
What is Your Metric Telling You? Evaluating Classifier Calibration under Context-Specific Definitions of Reliability—0
Better Classifier Calibration for Small Data Sets—0
Binary Classifier Calibration: Bayesian Non-Parametric Approach—0
Binary Classifier Calibration: Non-parametric approach—0
Binary Classifier Calibration using an Ensemble of Near Isotonic Regression Models—0
Classifier Calibration: A survey on how to assess and improve predicted class probabilities—0
Classifier Calibration with ROC-Regularized Isotonic Regression—0
Decoupling Decision-Making in Fraud Prevention through Classifier Calibration for Business Logic Action—0
FedSA: A Unified Representation Learning via Semantic Anchors for Prototype-based Federated Learning—0
Improved User Identification through Calibrated Monte-Carlo DropoutCode0
Long-tailed Medical Diagnosis with Relation-aware Representation Learning and Iterative Classifier CalibrationCode0
Hidden Heterogeneity: When to Choose Similarity-Based CalibrationCode0
Expeditious Saliency-guided Mix-up through Random Gradient ThresholdingCode0
Enhancing Generalized Few-Shot Semantic Segmentation via Effective Knowledge TransferCode0
Class-wise and reduced calibration methodsCode0
No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID DataCode0
Packed-Ensembles for Efficient Uncertainty EstimationCode0
Classifier Calibration: with application to threat scores in cybersecurityCode0
Accuracy-Preserving Calibration via Statistical Modeling on Probability SimplexCode0
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