Set2Model Networks: Learning Discriminatively to Learn Generative Models

Andrey Kuzmin, Alexander Vakhitov, Victor Lempitsky

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    Abstract

    We present a new 'learning-to-learn'-type approach for small-to-medium sized training sets. At the core lies a deep architecture (a Set2Model network) that maps sets of examples to simple generative probabilistic models such as Gaussians or mixtures of Gaussians in the space of high-dimensional descriptors. The parameters of the embedding into the descriptor space are discriminatively trained in the end-to-end fashion. The main technical novelty of our approach is the derivation of the backprop process through the mixture model fitting. A trained Set2Model network facilitates learning in the cases when no negative examples are available, and whenever the concept being learned is polysemous or represented by noisy training sets. Among other experiments, we demonstrate that these properties allow Set2Model networks to pick visual concepts from the raw outputs of Internet image search engines better than a set of strong baselines.

    Original languageEnglish
    Title of host publicationProceedings - 2017 IEEE International Conference on Computer Vision Workshops, ICCVW 2017
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages357-366
    Number of pages10
    ISBN (Electronic)9781538610343
    DOIs
    Publication statusPublished - 1 Jul 2017
    Event16th IEEE International Conference on Computer Vision Workshops, ICCVW 2017 - Venice, Italy
    Duration: 22 Oct 201729 Oct 2017

    Publication series

    NameProceedings - 2017 IEEE International Conference on Computer Vision Workshops, ICCVW 2017
    Volume2018-January

    Conference

    Conference16th IEEE International Conference on Computer Vision Workshops, ICCVW 2017
    Country/TerritoryItaly
    CityVenice
    Period22/10/1729/10/17

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