Label Smoothing++: Enhanced Label Regularization for Training Neural Networks


Sachin Chhabra (Arizona State University), Hemanth Venkateswara (Georgia State University), Baoxin Li (Arizona State University)
The 35th British Machine Vision Conference
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Abstract

Training neural networks with one-hot target labels often results in overconfidence and overfitting. Label smoothing addresses this issue by perturbing the one-hot target labels by adding a uniform probability vector to create a regularized label. Although label smoothing improves the network's generalization ability, it assigns equal importance to all the non-target classes, which destroys the inter-class relationships. In this paper, we propose a novel label regularization training strategy called Label Smoothing++, which assigns non-zero probabilities to non-target classes and accounts for their inter-class relationships. Our approach uses a fixed label for the target class while enabling the network to learn the labels associated with non-target classes. Through extensive experiments on multiple datasets, we demonstrate how Label Smoothing++ mitigates overconfident predictions while promoting inter-class relationships and generalization capabilities.

Citation

@inproceedings{Chhabra_2024_BMVC,
author    = {Sachin Chhabra and Hemanth Venkateswara and Baoxin Li},
title     = {Label Smoothing++: Enhanced Label Regularization for Training Neural Networks},
booktitle = {35th British Machine Vision Conference 2024, {BMVC} 2024, Glasgow, UK, November 25-28, 2024},
publisher = {BMVA},
year      = {2024},
url       = {https://papers.bmvc2024.org/0392.pdf}
}


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