# Distributional Generalization: A New Kind of Generalization

@article{Nakkiran2020DistributionalGA, title={Distributional Generalization: A New Kind of Generalization}, author={Preetum Nakkiran and Yamini Bansal}, journal={ArXiv}, year={2020}, volume={abs/2009.08092} }

We introduce a new notion of generalization -- Distributional Generalization -- which roughly states that outputs of a classifier at train and test time are close *as distributions*, as opposed to close in just their average error. For example, if we mislabel 30% of dogs as cats in the train set of CIFAR-10, then a ResNet trained to interpolation will in fact mislabel roughly 30% of dogs as cats on the *test set* as well, while leaving other classes unaffected. This behavior is not captured by…

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