Improved Deep Metric Learning With Multi-Class N-Pair Loss Objective
NeurIPS 2016 | We tackle the problem of unsatisfactory convergence of training a deep neural network for metric learning by proposing multi-class N-pair loss. Unlike many other objective functions that ignore the information lying in the interconnections between the samples, N-pair loss utilizes full interaction of the examples from different classes within a batch. We also propose an efficient batch-construction strategy using only N-pairs of examples.
Collaborators: Kihyuk Sohn