Heterogeneous Double-Head Ensemble for Deep Metric Learning

Heterogeneous Double-Head Ensemble for Deep Metric Learning

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The structure of veuve ambal rose a multi-head ensemble has been employed by many algorithms in various applications including deep metric learning.However, their structures have been empirically designed in a simple way such as using the same head structure, which leads to a limited ensemble effect due to lack of head diversity.In this paper, for an elaborate design of the multi-head ensemble structure, we establish design concepts based on three structural factors: designing the feature layer for extracting the ensemble-favorable feature vector, designing the shared part for chicago cubs earrings memory savings, and designing the diverse multi-heads for performance improvement.Through rigorous evaluation of variants on the basis of the design concepts, we propose a heterogeneous double-head ensemble structure that drastically increases ensemble gain along with memory savings.

In verifying experiments on image retrieval datasets, the proposed ensemble structure outperforms the state-of-the-art algorithms by margins of over 5.3%, 6.1%, 5.9%, and 1.

8% in CUB-200, Car-196, SOP, and Inshop, respectively.