  This paper presents an iterative pruning strategy for Convolutional Network
Fabrics (CNF) in presence of noisy training and testing data. With the
continuous increase in size of neural network models, various authors have
developed pruning approaches to build more compact network structures requiring
less resources, while preserving performance. As we show in this paper, because
of their intrinsic structure and function, Convolutional Network Fabrics are
ideal candidates for pruning. We present a series of pruning strategies that
can significantly reduce both the final network size and required training time
by pruning either entire convolutional filters or individual weights, so that
the grid remains visually understandable but that overall execution quality
stays within controllable boundaries. Our approach can be iteratively applied
during training so that the network complexity decreases rapidly, saving
computational time. The paper addresses both data-dependent and dataindependent
strategies, and also experimentally establishes the most efficient approaches
when training or testing data contain annotation errors.
