  Previous research has shown that fully-connected networks with small
initialization and gradient-based training methods exhibit a phenomenon known
as condensation during training. This phenomenon refers to the input weights of
hidden neurons condensing into isolated orientations during training, revealing
an implicit bias towards simple solutions in the parameter space. However, the
impact of neural network structure on condensation has not been investigated
yet. In this study, we focus on the investigation of convolutional neural
networks (CNNs). Our experiments suggest that when subjected to small
initialization and gradient-based training methods, kernel weights within the
same CNN layer also cluster together during training, demonstrating a
significant degree of condensation. Theoretically, we demonstrate that in a
finite training period, kernels of a two-layer CNN with small initialization
will converge to one or a few directions. This work represents a step towards a
better understanding of the non-linear training behavior exhibited by neural
networks with specialized structures.

Machine Learning;Neural and Evolutionary Computing