Delving Deep Into Label Smoothing

نویسندگان

چکیده

Label smoothing is an effective regularization tool for deep neural networks (DNNs), which generates soft labels by applying a weighted average between the uniform distribution and hard label. It often used to reduce overfitting problem of training DNNs further improve classification performance. In this paper, we aim investigate how generate more reliable labels. We present Online Smoothing (OLS) strategy, based on statistics model prediction target category. The proposed OLS constructs reasonable probability categories non-target supervise DNNs. Experiments demonstrate that same models, approach can effectively performance CIFAR-100, ImageNet, fine-grained datasets. Additionally, method significantly robustness DNN models noisy compared current label approaches.

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ژورنال

عنوان ژورنال: IEEE transactions on image processing

سال: 2021

ISSN: ['1057-7149', '1941-0042']

DOI: https://doi.org/10.1109/tip.2021.3089942