1 research outputs found
Delving Deep into Label Smoothing
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 the hard label. It is often used to reduce the
overfitting problem of training DNNs and further improve classification
performance. In this paper, we aim to investigate how to generate more reliable
soft labels. We present an Online Label Smoothing (OLS) strategy, which
generates soft labels based on the statistics of the model prediction for the
target category. The proposed OLS constructs a more reasonable probability
distribution between the target categories and non-target categories to
supervise DNNs. Experiments demonstrate that based on the same classification
models, the proposed approach can effectively improve the classification
performance on CIFAR-100, ImageNet, and fine-grained datasets. Additionally,
the proposed method can significantly improve the robustness of DNN models to
noisy labels compared to current label smoothing approaches.Comment: 12 pages, 7 figures, 12 table