Deep embedding learning with adaptive large margin N-pair loss for image retrieval and clustering

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, Adaptive Large Margin N-Pair loss (ALMN) is proposed to address the aforementioned issues. First, the class center is adopted as the anchor point to avoid the difficulty on anchor selection. Then instead of exploring hard example-mining strategy, we introduce the adaptive large margin constraint, where a novel geometrical Virtual Point Generating (VPG) method is proposed to convert a fixed margin into a local-adaptive angular margin, by automatically generating a boundary training sample in feature space. The effectiveness of our method is demonstrated on fine-grained image retrieval and clustering tasks using six popular databases, including CUB, CARS, Flowers, Aircraft, Stanford Online Products and In-Shop Clothes. The results show that the proposed method achieves better performance than other state-of-the-art methods, such as N-Pair loss, Lifted loss and Triplet loss. (C) 2019 Elsevier Ltd. All rights reserved.