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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).
관련 논문에서 소개한 기법입니다. 이 태스크에서의 평가 여부는 평가 결과에서 확인하세요.
R-Mix (Random Mix-up) is a Mix-up family Data Augmentation method. It combines random Mix-up with Saliency-guided mix-up, producing a procedure that is fast and performant, while reserving good characteristics of Saliency-guided Mix-up such as low Expected Calibration Error and high Weakly-supervised Object Localization accuracy.
Expeditious Saliency-guided Mix-up through Random Gradient Thresholding
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