• Corpus ID: 1396647

In Defense of the Triplet Loss for Person Re-Identification

@article{Hermans2017InDO,
  title={In Defense of the Triplet Loss for Person Re-Identification},
  author={Alexander Hermans and Lucas Beyer and B. Leibe},
  journal={ArXiv},
  year={2017},
  volume={abs/1703.07737}
}
In the past few years, the field of computer vision has gone through a revolution fueled mainly by the advent of large datasets and the adoption of deep convolutional neural networks for end-to-end learning. [] Key Result We show that, for models trained from scratch as well as pretrained ones, using a variant of the triplet loss to perform end-to-end deep metric learning outperforms most other published methods by a large margin.

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