The tremendous success of generative models in recent years raises the question whether they can also be used to perform classification. Generative models have been used as adversarially robust classifiers on simple datasets such as MNIST, but this robustness has not been observed on more complex datasets like CIFAR-10. Additionally, on natural image datasets, previous results have suggested a trade-off between the likelihood of the data and classification accuracy. In this work, we investigate score-based generative models as classifiers for natural images. We show that these models not only obtain competitive likelihood values but simultaneously achieve state-of-the-art classification accuracy for generative classifiers on CIFAR-10. Nevertheless, we find that these models are only slightly, if at all, more robust than discriminative baseline models on out-of-distribution tasks based on common image corruptions. Similarly and contrary to prior results, we find that score-based are prone to worst-case distribution shifts in the form of adversarial perturbations. Our work highlights that score-based generative models are closing the gap in classification accuracy compared to standard discriminative models. While they do not yet deliver on the promise of adversarial and out-of-domain robustness, they provide a different approach to classification that warrants further research.
翻译:近年来,基因模型的巨大成功引出了这样一个问题:这些模型是否也可以用于进行分类。在诸如MNIST等简单数据集中,作为对抗性强的分类器,在诸如MNIST这样的简单数据集中,已经作为对抗性强的分类器使用过,但是在诸如CIFAR-10等更复杂的数据集中,并没有观察到这种稳健性。此外,在自然图像数据集中,先前的结果表明数据的可能性和分类准确性之间存在权衡。在这项工作中,我们把基于分数的基因模型作为自然图像分类器来调查。我们显示,这些模型不仅获得有竞争力的可能性值,而且同时在CIFAR-10的基因分类器中也实现了最先进的分类精准性分类。然而,我们发现这些模型只是略微强,甚至比基于共同图像腐败的分配任务的歧视性基线模型强得多。与以往的结果相反,我们发现基于分法的模型容易发生最坏的分布变化,即以对抗性扰动形式出现。我们的工作突出表明,基于分法的基因化模型正在缩小分类准确性与标准歧视性模型之间的差距。虽然它们并没有提供一种坚定的反向研究的承诺。