  Although the recent progress in the deep neural network has led to the
development of learnable local feature descriptors, there is no explicit answer
for estimation of the necessary size of a neural network. Specifically, the
local feature is represented in a low dimensional space, so the neural network
should have more compact structure. The small networks required for local
feature descriptor learning may be sensitive to initial conditions and learning
parameters and more likely to become trapped in local minima. In order to
address the above problem, we introduce an adaptive pruning Siamese
Architecture based on neuron activation to learn local feature descriptors,
making the network more computationally efficient with an improved recognition
rate over more complex networks. Our experiments demonstrate that our learned
local feature descriptors outperform the state-of-art methods in patch
matching.
