Classification-Denoising Networks

• URL: http://arxiv.org/abs/2410.03505v1
• Date: Fri, 4 Oct 2024 15:20:57 GMT
• Title: Classification-Denoising Networks
• Authors: Louis Thiry, Florentin Guth,
• Abstract summary: Image classification and denoising suffer from complementary issues of lack of robustness or partially ignoring conditioning information. We argue that they can be alleviated by unifying both tasks through a model of the joint probability of (noisy) images and class labels. Numerical experiments on CIFAR-10 and ImageNet show competitive classification and denoising performance.
• Score: 6.783232060611113
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Image classification and denoising suffer from complementary issues of lack of robustness or partially ignoring conditioning information. We argue that they can be alleviated by unifying both tasks through a model of the joint probability of (noisy) images and class labels. Classification is performed with a forward pass followed by conditioning. Using the Tweedie-Miyasawa formula, we evaluate the denoising function with the score, which can be computed by marginalization and back-propagation. The training objective is then a combination of cross-entropy loss and denoising score matching loss integrated over noise levels. Numerical experiments on CIFAR-10 and ImageNet show competitive classification and denoising performance compared to reference deep convolutional classifiers/denoisers, and significantly improves efficiency compared to previous joint approaches. Our model shows an increased robustness to adversarial perturbations compared to a standard discriminative classifier, and allows for a novel interpretation of adversarial gradients as a difference of denoisers.

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