Open AccessProceedings Article
Hamming Distance Metric Learning
Mohammad Norouzi,David J. Fleet,Ruslan Salakhutdinov +2 more
- Vol. 25, pp 1061-1069
TLDR
A new loss-augmented inference algorithm that is quadratic in the code length and inspired by latent structural SVMs is developed, showing strong retrieval performance on CIFAR-10 and MNIST, with promising classification results using no more than kNN on the binary codes.Abstract:
Motivated by large-scale multimedia applications we propose to learn mappings from high-dimensional data to binary codes that preserve semantic similarity. Binary codes are well suited to large-scale applications as they are storage efficient and permit exact sub-linear kNN search. The framework is applicable to broad families of mappings, and uses a flexible form of triplet ranking loss. We overcome discontinuous optimization of the discrete mappings by minimizing a piecewise-smooth upper bound on empirical loss, inspired by latent structural SVMs. We develop a new loss-augmented inference algorithm that is quadratic in the code length. We show strong retrieval performance on CIFAR-10 and MNIST, with promising classification results using no more than kNN on the binary codes.read more
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