Calibration for Gingivitis Binary Classifier via Epoch-wise Decaying Label-Smoothing

라벨 스무딩을 활용한 치은염 이진 분류기 캘리브레이션

Future healthcare systems will heavily rely on ill-labeled data due to scarcity of the experts who are trained enough to label the data. Considering the contamination of the dataset, it is not desirable to make the neural network being overconfident to the dataset, but rather giving them some margins for the prediction is preferable. In this paper, we propose a novel epoch-wise decaying label-smoothing function to alleviate the model over-confidency, and it outperforms the neural network trained with conventional cross entropy by 6.0%.