S2SD: Simultaneous Similarity-based Self-Distillation for Deep Metric Learning

Deep Metric Learning (DML) provides a crucial tool for visual similarity and zero-shot applications by learning generalizing embedding spaces, although recent work in DML has shown strong performance saturation across training objectives. However, generalization capacity is known to scale with the e...

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Bibliographic Details
Main Authors: Roth, Karsten, Milbich, Timo, Ommer, Björn, Cohen, Joseph Paul, Ghassemi, Marzyeh
Format: Article in Journal/Newspaper
Language:unknown
Published: arXiv 2020
Subjects:
DML
Online Access:https://dx.doi.org/10.48550/arxiv.2009.08348
https://arxiv.org/abs/2009.08348
Description
Summary:Deep Metric Learning (DML) provides a crucial tool for visual similarity and zero-shot applications by learning generalizing embedding spaces, although recent work in DML has shown strong performance saturation across training objectives. However, generalization capacity is known to scale with the embedding space dimensionality. Unfortunately, high dimensional embeddings also create higher retrieval cost for downstream applications. To remedy this, we propose \emph{Simultaneous Similarity-based Self-distillation (S2SD). S2SD extends DML with knowledge distillation from auxiliary, high-dimensional embedding and feature spaces to leverage complementary context during training while retaining test-time cost and with negligible changes to the training time. Experiments and ablations across different objectives and standard benchmarks show S2SD offers notable improvements of up to 7% in Recall@1, while also setting a new state-of-the-art. Code available at https://github.com/MLforHealth/S2SD. : Accepted to ICML2021