Understanding Deep Learning via Decision Boundary

This article discovers that the neural network (NN) with lower decision boundary (DB) variability has better generalizability. Two new notions,
algorithm DB variability
and
$(\epsilon, \eta)$
-
data DB variability
, are proposed to measure the DB variability from the algorithm and data perspectives. Extensive experiments show significant negative correlations between the DB variability and the generalizability. From the theoretical view, two lower bounds based on algorithm DB variability are proposed and do not explicitly depend on the sample size. We also prove an upper bound of order
$\mathcal{O}\left(({1}/{\sqrt{m}})+\epsilon+\eta\log({1}/{\eta})\right)$
based on data DB variability. The bound is convenient to estimate without the requirement of labels and does not explicitly depend on the network size which is usually prohibitively large in deep learning.