  At present, the great achievements of convolutional neural network(CNN) in
feature and metric learning have attracted many researchers. However, the vast
majority of deep network architectures have been used to represent based on
real values. The research of complex-valued networks is seldom concerned due to
the absence of effective models and suitable distance of complex-valued vector.
Motived by recent works, complex vectors have been shown to have a richer
representational capacity and efficient complex blocks have been reported, we
propose a new approach for learning image descriptors with complex numbers to
compare image patches. We also propose a new architecture to learn image
similarity function directly based on complex-valued network. We show that our
models can perform competitive results on benchmark datasets. We make the
source code of our models publicly available.
