统一图像分类与去噪任务，提升模型鲁棒性与效率。
Classification-Denoising Networks
- 联合建模噪声图像与类别标签的联合概率分布
- 在CIFAR-10和ImageNet上达到媲美专用模型的性能
- 对对抗攻击更鲁棒，且可解释对抗梯度为去噪器差异
图像分类与去噪分别面临鲁棒性不足和条件信息忽略的问题。本文提出通过联合建模（噪声）图像与类别标签的联合概率分布来缓解这些问题。分类通过前向传播并进行条件化实现；利用Tweedie-Miyasawa公式，通过得分计算去噪函数，该得分可通过边际化与反向传播获得。训练目标为交叉熵损失与在多个噪声水平下积分的去噪得分匹配损失之和。在CIFAR-10与ImageNet上的实验表明，该模型在分类与去噪性能上均达到与专用深度卷积模型相当的水平，且相比以往联合方法显著提升效率。模型对对抗扰动表现出更强鲁棒性，并可将对抗梯度新颖地解释为两个去噪器之间的差异。
原文摘要 · Abstract (English)
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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