SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1–10 of 29 papers

TitleStatusHype
PrePrompt: Predictive prompting for class incremental learningCode1
No Fear of Classifier Biases: Neural Collapse Inspired Federated Learning with Synthetic and Fixed ClassifierCode1
FedFA: Federated Learning with Feature Anchors to Align Features and Classifiers for Heterogeneous DataCode1
Generalized and Incremental Few-Shot Learning by Explicit Learning and Calibration without ForgettingCode1
Danish Fungi 2020 -- Not Just Another Image Recognition DatasetCode1
Masksembles for Uncertainty EstimationCode1
How Well Do Self-Supervised Models Transfer?Code1
Multivariate Confidence Calibration for Object DetectionCode1
Multi-class probabilistic classification using inductive and cross Venn-Abers predictorsCode1
Long-tailed Medical Diagnosis with Relation-aware Representation Learning and Iterative Classifier CalibrationCode0
Show:102550
← PrevPage 1 of 3Next →

No leaderboard results yet.