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Found 5 papers, 1 papers with code
A Reproducible and Realistic Evaluation of Partial Domain Adaptation Methods
In this work, we consider the Partial Domain Adaptation (PDA) variant, where we have extra source classes not present in the target domain.
FairCal: Fairness Calibration for Face Verification
However, they still have drawbacks: they reduce accuracy (AGENDA, PASS, FTC), or require retuning for different false positive rates (FSN).
Frustratingly Easy Uncertainty Estimation for Distribution Shift
While the primary goal is to improve accuracy under distribution shift, an important secondary goal is uncertainty estimation: evaluating the probability that the prediction of a model is correct.
Uncertainty for deep image classifiers on out of distribution data.
In addition to achieving high accuracy, in many applications, it is important to estimate the probability that a model prediction is correct.
Calibrated Top-1 Uncertainty estimates for classification by score based models
While the accuracy of modern deep learning models has significantly improved in recent years, the ability of these models to generate uncertainty estimates has not progressed to the same degree.
