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Minimal Loss Hashing for Compact Binary Codes

2020-02-27 | |  55 |   39 |   0

Abstract

We propose a method for learning similaritypreserving hash functions that map highdimensional data onto binary codes. The formulation is based on structured prediction with latent variables and a hinge-like loss function. It is efficient to train for large datasets, scales well to large code lengths, and outperforms state-of-the-art methods.

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