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 21–29 of 29 papers

TitleStatusHype
Masksembles for Uncertainty EstimationCode1
How Well Do Self-Supervised Models Transfer?Code1
Multivariate Confidence Calibration for Object DetectionCode1
Better Classifier Calibration for Small Data Sets—0
High Frequency Residual Learning for Multi-Scale Image Classification—0
Multi-class probabilistic classification using inductive and cross Venn-Abers predictorsCode1
Binary Classifier Calibration using an Ensemble of Near Isotonic Regression Models—0
Binary Classifier Calibration: Non-parametric approach—0
Binary Classifier Calibration: Bayesian Non-Parametric Approach—0
Show:102550
← PrevPage 3 of 3Next →

No leaderboard results yet.