  The flexibility of decision boundaries in neural networks that are unguided
by training data is a well-known problem typically resolved with generalization
methods. A surprising result from recent knowledge distillation (KD) literature
is that random, untrained, and equally structured teacher networks can also
vastly improve generalization performance. It raises the possibility of
existence of undiscovered assumptions useful for generalization on an uncertain
region. In this paper, we shed light on the assumptions by analyzing decision
boundaries and confidence distributions of both simple and KD-based
generalization methods. Assuming that a decision boundary exists to represent
the most general tendency of distinction on an input sample space (i.e., the
simplest hypothesis), we show the various limitations of methods when using the
hypothesis. To resolve these limitations, we propose matured dumb teacher based
KD, conservatively transferring the hypothesis for generalization of the
student without massive destruction of trained information. In practical
experiments on feed-forward and convolution neural networks for image
classification tasks on MNIST, CIFAR-10, and CIFAR-100 datasets, the proposed
method shows stable improvement to the best test performance in the grid search
of hyperparameters. The analysis and results imply that the proposed method can
provide finer generalization than existing methods.
