{"ID":2879615,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.05307","arxiv_id":"2509.05307","title":"Label Smoothing++: Enhanced Label Regularization for Training Neural Networks","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.","short_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,...","url_abs":"https://arxiv.org/abs/2509.05307","url_pdf":"https://arxiv.org/pdf/2509.05307v1","authors":"[\"Sachin Chhabra\",\"Hemanth Venkateswara\",\"Baoxin Li\"]","published":"2025-08-22T23:11:43Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false}