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Mining on Manifolds: Metric Learning without Labels

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F18%3A00327169" target="_blank" >RIV/68407700:21230/18:00327169 - isvavai.cz</a>

  • Result on the web

    <a href="https://ieeexplore.ieee.org/document/8578895" target="_blank" >https://ieeexplore.ieee.org/document/8578895</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1109/CVPR.2018.00797" target="_blank" >10.1109/CVPR.2018.00797</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Mining on Manifolds: Metric Learning without Labels

  • Original language description

    In this work we present a novel unsupervised framework for hard training example mining. The only input to the method is a collection of images relevant to the target application and a meaningful initial representation, provided e.g. by pre-trained CNN. Positive examples are distant points on a single manifold, while negative examples are nearby points on different manifolds. Both types of examples are revealed by disagreements between Euclidean and manifold similarities. The discovered examples can be used in training with any discriminative loss. The method is applied to unsupervised fine-tuning of pre-trained networks for fine-grained classification and particular object retrieval. Our models are on par or are outperforming prior models that are fully or partially supervised.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

    <a href="/en/project/LL1303" target="_blank" >LL1303: Large Scale Category Retrieval</a><br>

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Others

  • Publication year

    2018

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    CVPR 2018: Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition

  • ISBN

    978-1-5386-6420-9

  • ISSN

    1063-6919

  • e-ISSN

    2575-7075

  • Number of pages

    10

  • Pages from-to

    7642-7651

  • Publisher name

    IEEE

  • Place of publication

    Piscataway, NJ

  • Event location

    Salt Lake City

  • Event date

    Jun 19, 2018

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article

    000457843607082