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Learning Compact Binary Descriptors with Unsupervised Deep Neural Networks
2016
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
In this paper, we propose a new unsupervised deep learning approach called DeepBit to learn compact binary descriptor for efficient visual object matching. Unlike most existing binary descriptors which were designed with random projections or linear hash functions, we develop a deep neural network to learn binary descriptors in an unsupervised manner. We enforce three criterions on binary codes which are learned at the top layer of our network: 1) minimal loss quantization, 2) evenly
doi:10.1109/cvpr.2016.133
dblp:conf/cvpr/LinLCZ16
fatcat:2mmikjjpkjdxtfniyyuvwo6ylq