text: 314,152,1445007474327793665,732494566545203201,David Klindt,"New paper on score-based generative classifiers (SBGCs) <LINK> Diffusion models have produced impressive results <LINK> We show how they can be used as classifiers on CIFAR-10. Work w/ @zimmerrol @schott_lukas @YsongStanford @adric_dunn (1/6) While previous methods have shown a trade-off between generative and classification performance, our SBGC model achieves new state-of-the-art performances both in likelihoods and classification accuracy for generative classifiers on CIFAR-10. (2/6) <LINK> In the past, generative classifiers (analysis-by-synthesis) have been shown to increase adversarial robustness on MNIST <LINK> However, so far these results have not been extended to complex natural image datasets such as CIFAR-10. (3/6) Previous work showed that interpolating between images increases likelihoods, suggesting model failure on out-of-distribution data <LINK> @jh_jacobsen By contrast, our SBGC model correctly produces convex interpolation curves. (4/6) <LINK> Nevertheless, we find that our model spectacularly fails against gradient-based adversarial attacks. We argue that SBGCs have no structural advantage over discriminative classifiers and that analysis-by-synthesis alone is not sufficient for out-of-distribution robustness. (5/6) <LINK> Still, our work shows that SBGCs can achieve very competitive likelihoods and classification accuracies which encourage further research! Thanks for fun discussions and feedback @poolio @wgrathwohl @yash_j_sharma @wielandbr @dylanpaiton @eero_simoncelli (6/6)",http://arxiv.org/abs/2110.00473,"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. ",Score-Based Generative Classifiers,6,"['New paper on score-based generative classifiers (SBGCs) <LINK>\n\nDiffusion models have produced impressive results <LINK>\n\nWe show how they can be used as classifiers on CIFAR-10.\n\nWork w/ @zimmerrol @schott_lukas @YsongStanford @adric_dunn\n\n(1/6)', 'While previous methods have shown a trade-off between generative and classification performance, our SBGC model achieves new state-of-the-art performances both in likelihoods and classification accuracy for generative classifiers on CIFAR-10.\n\n(2/6) https://t.co/iRl9XCTRAK', 'In the past, generative classifiers (analysis-by-synthesis) have been shown to increase adversarial robustness on MNIST https://t.co/OSMjtHjHX1\n\nHowever, so far these results have not been extended to complex natural image datasets such as CIFAR-10.\n\n(3/6)', 'Previous work showed that interpolating between images increases likelihoods, suggesting model failure on out-of-distribution data https://t.co/WBwvfWUSsK @jh_jacobsen\n\nBy contrast, our SBGC model correctly produces convex interpolation curves.\n\n(4/6) https://t.co/KWWUDkRBSI', 'Nevertheless, we find that our model spectacularly fails against gradient-based adversarial attacks.\n\nWe argue that SBGCs have no structural advantage over discriminative classifiers and that analysis-by-synthesis alone is not sufficient for out-of-distribution robustness.\n\n(5/6) https://t.co/TCZTbPiuIy', 'Still, our work shows that SBGCs can achieve very competitive likelihoods and classification accuracies which encourage further research!\n\nThanks for fun discussions and feedback @poolio @wgrathwohl @yash_j_sharma @wielandbr @dylanpaiton @eero_simoncelli\n\n(6/6)']",21,10,1522