Ranked List Loss for Deep Metric Learning.
The objective of deep metric learning (DML) is to learn embeddings that can capture semantic similarity/dissimilarity information among data points. Existing pairwise or tripletwise loss functions used in DML are known to suffer from slow convergence due to a large proportion of trivial pairs or triplets as the model improves. To improve this, ranking motivated structured losses are proposed recently to incorporate multiple examples and exploit the structured information among them. In this work, we unveil two limitations of existing ranking motivated structured losses and propose a novel ranked list loss to solve both of them. First, given a query, only a fraction of data points is incorporated to build the similarity structure. To address this, we propose to build a set based similarity structure by exploiting all instances in the gallery. Second, previous methods aim to pull positive pairs as close as possible in the embedding space. As a result, the intraclass data distribution tends to be extremely compressed. In contrast, we propose to learn a hypersphere for each class to preserve useful similarity structure inside it, which functions as regularisation. Extensive experiments demonstrate the superiority of our proposal by comparing with the state of the art methods on the fine grained image retrieval task.
IEEE transactions on pattern analysis and machine intelligence