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Characterizing Generalization under Out-Of-Distribution Shifts in Deep Metric Learning
Timo Milbich · Karsten Roth · Samarth Sinha · Ludwig Schmidt · Marzyeh Ghassemi · Bjorn Ommer

Thu Dec 09 08:30 AM -- 10:00 AM (PST) @

Deep Metric Learning (DML) aims to find representations suitable for zero-shot transfer to a priori unknown test distributions. However, common evaluation protocols only test a single, fixed data split in which train and test classes are assigned randomly. More realistic evaluations should consider a broad spectrum of distribution shifts with potentially varying degree and difficulty.In this work, we systematically construct train-test splits of increasing difficulty and present the ooDML benchmark to characterize generalization under out-of-distribution shifts in DML. ooDML is designed to probe the generalization performance on much more challenging, diverse train-to-test distribution shifts. Based on our new benchmark, we conduct a thorough empirical analysis of state-of-the-art DML methods. We find that while generalization tends to consistently degrade with difficulty, some methods are better at retaining performance as the distribution shift increases. Finally, we propose few-shot DML as an efficient way to consistently improve generalization in response to unknown test shifts presented in ooDML.

Author Information

Timo Milbich (LMU Munich & Heidelberg University)
Karsten Roth (University of Tuebingen)
Samarth Sinha (University of Toronto, Vector Institute)
Ludwig Schmidt (University of Washington)
Marzyeh Ghassemi (University of Toronto, Vector Institute)
Bjorn Ommer (Heidelberg University)

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