In machine learning, the hamming distance measures in machine learning the similarity between two strings of the same length. The hamming distance is the number of positions at which the corresponding characters are different. Since the length of these strings is equal, we can calculate the Hamming Distance.
Jun 22, 2024
Motivated by large-scale multimedia applications we propose to learn mappings from high-dimensional data to binary codes that preserve semantic similarity.
The Hamming distance, a natural similarity measure on binary codes, can be computed with just a few machine instructions per comparison.
Motivated by large-scale multimedia applications we propose to learn mappings from high-dimensional data to binary codes that preserve semantic similarity.
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We trained our Hamming distance metric learning framework on 6400-dimensional bag-of-word features extracted from the CIFAR-10 training images.
In a more general context, the Hamming distance is one of several string metrics for measuring the edit distance between two sequences. It is named after the ...
Aug 30, 2017 · The paper presents a framework for Hamming distance metric learning, which entails learning a discrete mapping from the input space onto binary ...
Oct 30, 2015 · Motivated by large-scale multimedia applications we propose to learn mappings from high-dimensional data to binary codes that preserve ...
A new loss-augmented inference algorithm that is quadratic in the code length and inspired by latent structural SVMs is developed, showing strong retrieval ...
Hamming Distance Metric Learning with Non-linear Projection - norouzi/hdml. ... This is an implementation of "Hamming Distance Metric Learning, NIPS 2012" method.
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