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Fast Training of Triplet-based Deep Binary Embedding Networks
[article]
2016
arXiv
pre-print
In this paper, we aim to learn a mapping (or embedding) from images to a compact binary space in which Hamming distances correspond to a ranking measure for the image retrieval task. We make use of a triplet loss because this has been shown to be most effective for ranking problems. However, training in previous works can be prohibitively expensive due to the fact that optimization is directly performed on the triplet space, where the number of possible triplets for training is cubic in the
arXiv:1603.02844v2
fatcat:j7adme72nbc2znonmvqnpsj3ua