Semantic hashing seeks compact binary codes of datapoints so that the Hamming distance between codewords correlates with semantic similarity. Hinton et al. used ...

Semantic hashing[1] seeks compact binary codes of data-points so that the. Hamming distance between codewords correlates with semantic similarity.

Spectral hash is insecure; a method exists to generate arbitrary collisions in the hash state, and therefore in the final hash digest.

A webpage dedicated to the latest research on learning-to-hash, including state-of-the-art deep hashing models, all updated on a weekly basis. Maintained by ...

Dec 8, 2008 · In this paper, we show that the problem of finding a best code for a given dataset is closely related to the problem of graph partitioning and ...

We show that this criterion is intractable to solve exactly, but a spectral relaxation gives an algorithm where the bits correspond to thresholded eigenvectors ...

Spectral hash is a new family of hash functions using the discrete Fourier transform and a nonlinear transformation constructed via data dependent permutations.

Spectral Hashing (NIPS '08). Assume points are embedded in Euclidean space ... How to binarize so Hamming distance approximates Euclidean distance? Ham_Dist( ...

This paper shows that the problem of finding a best code for a given dataset is closely related to the problem of graph partitioning and can be shown to be ...

Apr 26, 2013 · Abstract. Spectral hashing assigns binary hash keys to data points. This is accomplished via thresholding the eigenvectors of the graph ...