  With the remarkable success achieved by the Convolutional Neural Networks
(CNNs) in object recognition recently, deep learning is being widely used in
the computer vision community. Deep Metric Learning (DML), integrating deep
learning with conventional metric learning, has set new records in many fields,
especially in classification task. In this paper, we propose a replicable DML
method, called Include and Exclude (IE) loss, to force the distance between a
sample and its designated class center away from the mean distance of this
sample to other class centers with a large margin in the exponential feature
projection space. With the supervision of IE loss, we can train CNNs to enhance
the intra-class compactness and inter-class separability, leading to great
improvements on several public datasets ranging from object recognition to face
verification. We conduct a comparative study of our algorithm with several
typical DML methods on three kinds of networks with different capacity.
Extensive experiments on three object recognition datasets and two face
recognition datasets demonstrate that IE loss is always superior to other
mainstream DML methods and approach the state-of-the-art results.
