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Revisiting Additive Quantization

  • Julieta Martinez
  • Joris Clement
  • Holger H. Hoos
  • James J. Little
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9906)

Abstract

We revisit Additive Quantization (AQ), an approach to vector quantization that uses multiple, full-dimensional, and non-orthogonal codebooks. Despite its elegant and simple formulation, AQ has failed to achieve state-of-the-art performance on standard retrieval benchmarks, because the encoding problem, which amounts to MAP inference in multiple fully-connected Markov Random Fields (MRFs), has proven to be hard to solve. We demonstrate that the performance of AQ can be improved to surpass the state of the art by leveraging iterated local search, a stochastic local search approach known to work well for a range of NP-hard combinatorial problems. We further show a direct application of our approach to a recent formulation of vector quantization that enforces sparsity of the codebooks. Unlike previous work, which required specialized optimization techniques, our formulation can be plugged directly into state-of-the-art lasso optimizers. This results in a conceptually simple, easily implemented method that outperforms the previous state of the art in solving sparse vector quantization. Our implementation is publicly available (https://github.com/jltmtz/local-search-quantization).

Keywords

Local Search Query Time Local Search Procedure Inverted Index Iterate Local Search 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

We thank NVIDIA for the donation of some of the GPUs used in this project. Joris Clement was supported by DAAD while doing an internship at the University of British Columbia. This research was supported in part by NSERC.

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Julieta Martinez
    • 1
  • Joris Clement
    • 1
  • Holger H. Hoos
    • 1
  • James J. Little
    • 1
  1. 1.University of British ColumbiaVancouverCanada

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