These benchmarks typically assume large annotated training sets, little mismatch between training and test distributions, relatively clean data, and a lack of adversarial examples (Zue et al., 1990;Marcus et al., 1993;Deng et al., 2009;Lin et al., 2014).
when conditions are not ideal for discriminative classifiers, generative classifiers can actually perform better.