This is the final version. Available from ICLR via the link in this recordDeep neural networks (DNNs) generalize remarkably well without explicit regularization even in the strongly over-parametrized regime where classical learning
theory would instead predict that they would severely overfit. While many proposals for some kind of implicit regularization have been made to rationalise this success, there is no consensus for the fundamental reason why DNNs do not strongly
overfit. In this paper, we provide a new explanation. By applying a very general probability-complexity bound recently derived from algorithmic information
theory (AIT), we argue that the parameter-function map of many DNNs should be
exponentially biased towards simple functions. We then provide clear evidence for
this strong bias in a model DNN for Boolean functions, as well as in much larger
fully conected and convolutional networks trained on CIFAR10 and MNIST. As
the target functions in many real problems are expected to be highly structured,
this intrinsic simplicity bias helps explain why deep networks generalize well on
real world problems. This picture also facilitates a novel PAC-Bayes approach
where the prior is taken over the DNN input-output function space, rather than
the more conventional prior over parameter space. If we assume that the training algorithm samples parameters close to uniformly within the zero-error region
then the PAC-Bayes theorem can be used to guarantee good expected generalization for target functions producing high-likelihood training sets. By exploiting recently discovered connections between DNNs and Gaussian processes to estimate
the marginal likelihood, we produce relatively tight generalization PAC-Bayes error bounds which correlate well with the true error on realistic datasets such as
MNIST and CIFAR10and for architectures including convolutional and fully connected networks