Improving Deep Metric Learning by Divide and Conquer

  title={Improving Deep Metric Learning by Divide and Conquer},
  author={Artsiom Sanakoyeu and Pingchuan Ma and Vadim Tschernezki and Bj{\"o}rn Ommer},
  journal={IEEE transactions on pattern analysis and machine intelligence},
Deep metric learning aims at learning a mapping from the input domain to an embedding space, where semantically similar objects are located nearby and dissimilar objects far from another. However, while the embedding space learns to mimic the user-provided similarity on the training data, it should also generalize to novel categories not seen during training. Besides user-provided training labels, a lot of additional visual factors (such as viewpoint changes or shape peculiarities) exist and… 

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