Ordinal Adaptive Correction: A Data-Centric Approach to Ordinal Image Classification with Noisy Labels

In three linesORDAC, a data-centric method, corrects noisy labels in ordinal image classification using Label Distribution Learning. Tested on Adience (age estimation) and Diabetic Retinopathy (disease severity), ORDAC_R reduces mean absolute error from 0.86 to 0.62 with 40% noise.

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