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Fracking Deep Convolutional Image Descriptors
[article]
2015
arXiv
pre-print
In this paper we propose a novel framework for learning local image descriptors in a discriminative manner. For this purpose we explore a siamese architecture of Deep Convolutional Neural Networks (CNN), with a Hinge embedding loss on the L2 distance between descriptors. Since a siamese architecture uses pairs rather than single image patches to train, there exist a large number of positive samples and an exponential number of negative samples. We propose to explore this space with a stochastic
arXiv:1412.6537v2
fatcat:cwq34j4pyjcnxhfa6wviecsxxa