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Second-Order Democratic Aggregation

  • Tsung-Yu LinEmail author
  • Subhransu Maji
  • Piotr Koniusz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11207)

Abstract

Aggregated second-order features extracted from deep convolutional networks have been shown to be effective for texture generation, fine-grained recognition, material classification, and scene understanding. In this paper, we study a class of orderless aggregation functions designed to minimize interference or equalize contributions in the context of second-order features and we show that they can be computed just as efficiently as their first-order counterparts and they have favorable properties over aggregation by summation. Another line of work has shown that matrix power normalization after aggregation can significantly improve the generalization of second-order representations. We show that matrix power normalization implicitly equalizes contributions during aggregation thus establishing a connection between matrix normalization techniques and prior work on minimizing interference. Based on the analysis we present \(\gamma \)-democratic aggregators that interpolate between sum (\(\gamma \) = 1) and democratic pooling (\(\gamma \) = 0) outperforming both on several classification tasks. Moreover, unlike power normalization, the \(\gamma \)-democratic aggregations can be computed in a low dimensional space by sketching that allows the use of very high-dimensional second-order features. This results in a state-of-the-art performance on several datasets.

Keywords

Second-order features Democratic pooling Matrix power normalization Tensor sketching 

Notes

Acknowlegements

We acknowledge support from NSF (#1617917, #1749833) and the MassTech Collaborative grant for funding the UMass GPU cluster.

Supplementary material

474178_1_En_38_MOESM1_ESM.pdf (184 kb)
Supplementary material 1 (pdf 184 KB)

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.College of Information and Computer SciencesUniversity of Massachusetts AmherstAmherstUSA
  2. 2.Data61/CSIROAustralian National UniversityCanberraAustralia

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