Web: http://arxiv.org/abs/2209.06469

Sept. 15, 2022, 1:13 a.m. | Soumava Kumar Roy, Yan Han, Mehrtash Harandi, Lars Petersson

cs.CV updates on arXiv.org arxiv.org

Deep Metric Learning algorithms aim to learn an efficient embedding space to
preserve the similarity relationships among the input data. Whilst these
algorithms have achieved significant performance gains across a wide plethora
of tasks, they have also failed to consider and increase comprehensive
similarity constraints; thus learning a sub-optimal metric in the embedding
space. Moreover, up until now; there have been few studies with respect to
their performance in the presence of noisy labels. Here, we address the concern
of …


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