  Several works have shown that the regularization mechanisms underlying deep
neural networks' generalization performances are still poorly understood. In
this paper, we hypothesize that deep neural networks are regularized through
their ability to extract meaningful clusters among the samples of a class. This
constitutes an implicit form of regularization, as no explicit training
mechanisms or supervision target such behaviour. To support our hypothesis, we
design four different measures of intraclass clustering, based on the neuron-
and layer-level representations of the training data. We then show that these
measures constitute accurate predictors of generalization performance across
variations of a large set of hyperparameters (learning rate, batch size,
optimizer, weight decay, dropout rate, data augmentation, network depth and
width).
