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