  Deep embedding learning becomes more attractive for discriminative feature
learning, but many methods still require hard-class mining, which is
computationally complex and performance-sensitive. To this end, we propose
Adaptive Large Margin N-Pair loss (ALMN) to address the aforementioned issues.
Instead of exploring hard example-mining strategy, we introduce the concept of
large margin constraint. This constraint aims at encouraging local-adaptive
large angular decision margin among dissimilar samples in multimodal feature
space so as to significantly encourage intraclass compactness and interclass
separability. And it is mainly achieved by a simple yet novel geometrical
Virtual Point Generating (VPG) method, which converts artificially setting a
fixed margin into automatically generating a boundary training sample in
feature space and is an open question. We demonstrate the effectiveness of our
method on several popular datasets for image retrieval and clustering tasks.
