Method, server, and system for deep metric learning per hierarchical steps of multi-labels and few-shot inference using the same

Abstract

A method for deep metric learning per hierarchical step of multi-labels and few-shot inference using the same is provided, which includes sampling a plurality of N multiplets from a learning data set that is expressed by hierarchical multi-labels; configuring the N multiplets into triplets or quadruplets so as to correspond to the number of classes in each hierarchy; performing a metric learning per hierarchical step for a deep neural network model based on the configured triplets and quadruplets; and distributing, to an embedded device, the deep neural network model from which a loss function is removed as the metric learning is completed.