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Deep metric learning (DML) is a popular approach for images retrieval,solving verification (same or not) problems and addressing open setclassification. Arguably, the most common DML approach is with triplet loss,despite significant advances in the area of DML. Triplet loss suffers fromseveral issues such as collapse of the embeddings, high sensitivity to samplingschemes and more importantly a lack of performance when compared to more modernmethods. We attribute this adoption to a lack of fair comparisons betweenvarious methods and the difficulty in adopting them for novel problemstatements. In this paper, we perform an unbiased comparison of the mostpopular DML baseline methods under same conditions and more importantly, notobfuscating any hyper parameter tuning or adjustment needed to favor aparticular method. We find, that under equal conditions several older methodsperform significantly better than previously believed. In fact, our unifiedimplementation of 12 recently introduced DML algorithms achieve state-of-theart performance on CUB200, CAR196, and Stanford Online products datasets whichestablishes a new set of baselines for future DML research. The codebase andall tuned hyperparameters will be open-sourced for reproducibility and to serveas a source of benchmark.

Unbiased Evaluation of Deep Metric Learning Algorithms