Classification-Denoising Networks
<|reference_start|>Classification-Denoising Networks: 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.<|reference_end|>
@article{thiry2024classification-denoising,
    title={Classification-Denoising Networks},
    author={Louis Thiry and Florentin Guth},
    journal={arXiv preprint arXiv:2410.03505},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.03505},
    primaryClass={cs.CV cs.LG}
}