Deep Metric Learning

The Group Loss++: A Deeper Look Into Group Loss For Deep Metric Learning
We adjust Group Loss, and propose Group Loss++, providing a unified framework for retrieval and re-identification
Ismail Elezi, Jenny Seidenschwarz, Laurin Wagner, Sebastiano Vascon, Alessandro Torcinovich, Marcello Pelillo, Laura Leal-Taixé
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The Group Loss++: A Deeper Look Into Group Loss For Deep Metric Learning
The Group Loss for Deep Metric Embedding
We propose Group Loss, a new loss for deep metric learning tasks, that takes into account all the observations in a mini-batch at the same time
Ismail Elezi, Sebastiano Vascon, Alessandro Torcinovich, Marcello Pelillo, Laura Leal-Taixé
Cite DOI
The Group Loss for Deep Metric Embedding