Robust Classification by Coupling Data Mollification with Label Smoothing

• URL: http://arxiv.org/abs/2406.01494v3
• Date: Thu, 01 May 2025 14:25:15 GMT
• Title: Robust Classification by Coupling Data Mollification with Label Smoothing
• Authors: Markus Heinonen, Ba-Hien Tran, Michael Kampffmeyer, Maurizio Filippone,
• Abstract summary: 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.<n>We demonstrate improved robustness and uncertainty on the corrupted image benchmarks of CIFAR, TinyImageNet and ImageNet datasets.
• Score: 25.66357344079206
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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