<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sanakoyeu, A.</style></author><author><style face="normal" font="default" size="100%">Tschernezki, V.</style></author><author><style face="normal" font="default" size="100%">Uta Büchler</style></author><author><style face="normal" font="default" size="100%">Björn Ommer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Divide and Conquer the Embedding Space for Metric Learning</style></title><secondary-title><style face="normal" font="default" size="100%"> Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">deep learning</style></keyword><keyword><style  face="normal" font="default" size="100%">metric learning</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://github.com/CompVis/metric-learning-divide-and-conquer</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language></record></records></xml>