Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Stacked Quantizers for Compositional Vector Compression   Recently, Babenko and Lempitsky introduced Additive Quantization (AQ), a
generalization of Product Quantization (PQ) where a non-independent set of
codebooks is used to compress vectors into small binary codes. Unfortunately,
under this scheme encoding cannot be done independently in each codebook, and
optimal encoding is an NP-hard problem. In this paper, we observe that PQ and
AQ are both compositional quantizers that lie on the extremes of the codebook
dependence-independence assumption, and explore an intermediate approach that
exploits a hierarchical structure in the codebooks. This results in a method
that achieves quantization error on par with or lower than AQ, while being
several orders of magnitude faster. We perform a complexity analysis of PQ, AQ
and our method, and evaluate our approach on standard benchmarks of SIFT and
GIST descriptors, as well as on new datasets of features obtained from
state-of-the-art convolutional neural networks.

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The categories of this paper are: hep-lat
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The categories of this paper are: mtrl-th
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The categories of this paper are: supr-con
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The categories of this paper are: plasm-ph
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The categories of this paper are: q-fin
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The categories of this paper are: chao-dyn
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The categories of this paper are: q-alg
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The categories of this paper are: bayes-an
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The categories of this paper are: math
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The categories of this paper are: adap-org
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The categories of this paper are: comp-gas
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The categories of this paper are: ao-sci
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The categories of this paper are: physics
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The categories of this paper are: astro-ph
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The categories of this paper are: patt-sol
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The categories of this paper are: math-ph