Papers › Robust Classification by Coupling Data Mollification with Label Smoothing

Robust Classification by Coupling Data Mollification with Label Smoothing

3 Jun 2024arXiv:2406.01494archive 2025-07-28

Markus Heinonen, Ba-Hien Tran, Michael Kampffmeyer, Maurizio Filippone

Introducing training-time augmentations is a key technique to enhance generalization and prepare deep neural networks against test-time corruptions. Inspired by the success of generative diffusion models, we propose a novel approach of coupling data mollification, in the form of image noising and blurring, with label smoothing to align predicted label confidences with image degradation. The method is simple to implement, introduces negligible overheads, and can be combined with existing augmentations. We demonstrate improved robustness and uncertainty quantification on the corrupted image benchmarks of CIFAR, TinyImageNet and ImageNet datasets.

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