Abstract
Learning embedding functions, which map semantically related inputs to nearby locations in a feature space supports a variety of classification and information retrieval tasks. In this work, we propose a novel, generalizable and fast method to define a family of embedding functions that can be used as an ensemble to give improved results. Each embedding function is learned by randomly bagging the training labels into small subsets. We show experimentally that these embedding ensembles create effective embedding functions. The ensemble output defines a metric space that improves state of the art performance for image retrieval on CUB-200-2011, Cars-196, In-Shop Clothes Retrieval and VehicleID.
Abstract (translated)
学习嵌入功能，将语义相关的输入映射到特征空间中的附近位置，支持各种分类和信息检索任务。在这项工作中，我们提出了一种新颖的，可推广的快速方法来定义一系列嵌入函数，这些函数可以用作集合来改善结果。通过将训练标签随机包装成小的子集来学习每个嵌入功能。我们通过实验证明这些嵌入集合可以创建有效的嵌入函数。整体输出定义了一个度量空间，可以改善CUB-200-2011，Cars-196，店内衣服检索和VehicleID上图像检索的最先进性能。
URL
https://arxiv.org/abs/1808.04469
