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In this paper, we explore the application of mean field theory, a techniquefrom statistical physics, to deep metric learning and address the high trainingcomplexity commonly associated with conventional metric learning lossfunctions. By adapting mean field theory for deep metric learning, we developan approach to design classification-based loss functions from pair-based ones,which can be considered complementary to the proxy-based approach. Applying themean field theory to two pair-based loss functions, we derive two new lossfunctions, MeanFieldContrastive and MeanFieldClassWiseMultiSimilarity losses,with reduced training complexity. We extensively evaluate these derived lossfunctions on three image-retrieval datasets and demonstrate that our lossfunctions outperform baseline methods in two out of the three datasets.

Mean Field Theory in Deep Metric Learning