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• W3204285762 abstract "The tremendous success of generative models in recent years raises thequestion whether they can also be used to perform classification. Generativemodels have been used as adversarially robust classifiers on simple datasetssuch as MNIST, but this robustness has not been observed on more complexdatasets like CIFAR-10. Additionally, on natural image datasets, previousresults have suggested a trade-off between the likelihood of the data andclassification accuracy. In this work, we investigate score-based generativemodels as classifiers for natural images. We show that these models not onlyobtain competitive likelihood values but simultaneously achievestate-of-the-art classification accuracy for generative classifiers onCIFAR-10. Nevertheless, we find that these models are only slightly, if at all,more robust than discriminative baseline models on out-of-distribution tasksbased on common image corruptions. Similarly and contrary to prior results, wefind that score-based are prone to worst-case distribution shifts in the formof adversarial perturbations. Our work highlights that score-based generativemodels are closing the gap in classification accuracy compared to standarddiscriminative models. While they do not yet deliver on the promise ofadversarial and out-of-domain robustness, they provide a different approach toclassification that warrants further research." @default.
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• W3204285762 date "2021-10-01" @default.
• W3204285762 modified "2023-09-27" @default.
• W3204285762 title "Score-Based Generative Classifiers" @default.
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