  In this paper, we propose a novel training strategy for convolutional neural
network(CNN) named Feature Mining, that aims to strengthen the network's
learning of the local feature. Through experiments, we find that semantic
contained in different parts of the feature is different, while the network
will inevitably lose the local information during feedforward propagation. In
order to enhance the learning of local feature, Feature Mining divides the
complete feature into two complementary parts and reuse these divided feature
to make the network learn more local information, we call the two steps as
feature segmentation and feature reusing. Feature Mining is a parameter-free
method and has plug-and-play nature, and can be applied to any CNN models.
Extensive experiments demonstrate the wide applicability, versatility, and
compatibility of our method.
