  Deep metric learning, which learns discriminative features to process image
clustering and retrieval tasks, has attracted extensive attention in recent
years. A number of deep metric learning methods, which ensure that similar
examples are mapped close to each other and dissimilar examples are mapped
farther apart, have been proposed to construct effective structures for loss
functions and have shown promising results. In this paper, different from the
approaches on learning the loss structures, we propose a robust SNR distance
metric based on Signal-to-Noise Ratio (SNR) for measuring the similarity of
image pairs for deep metric learning. By exploring the properties of our SNR
distance metric from the view of geometry space and statistical theory, we
analyze the properties of our metric and show that it can preserve the semantic
similarity between image pairs, which well justify its suitability for deep
metric learning. Compared with Euclidean distance metric, our SNR distance
metric can further jointly reduce the intra-class distances and enlarge the
inter-class distances for learned features. Leveraging our SNR distance metric,
we propose Deep SNR-based Metric Learning (DSML) to generate discriminative
feature embeddings. By extensive experiments on three widely adopted
benchmarks, including CARS196, CUB200-2011 and CIFAR10, our DSML has shown its
superiority over other state-of-the-art methods. Additionally, we extend our
SNR distance metric to deep hashing learning, and conduct experiments on two
benchmarks, including CIFAR10 and NUS-WIDE, to demonstrate the effectiveness
and generality of our SNR distance metric.
