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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