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Learning with Noisy and Trusted Labels for Fine-Grained Plant Recognition

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F17%3A00315167" target="_blank" >RIV/68407700:21230/17:00315167 - isvavai.cz</a>

  • Result on the web

    <a href="http://ceur-ws.org/Vol-1866/paper_167.pdf" target="_blank" >http://ceur-ws.org/Vol-1866/paper_167.pdf</a>

  • DOI - Digital Object Identifier

Alternative languages

  • Result language

    angličtina

  • Original language name

    Learning with Noisy and Trusted Labels for Fine-Grained Plant Recognition

  • Original language description

    The paper describes the deep learning approach to automatic visual recognition of 10 000 plant species submitted to the PlantCLEF 2017 challenge. We evaluate modifications and extensions of the state-ofthe-art Inception-ResNet-v2 CNN architecture, including maxout, bootstrapping for training with noisy labels, and filtering the data with noisy labels using a classifier pre-trained on the trusted dataset. The final pipeline consists of a set of CNNs trained with different modifications on different subsets of the provided training data. With the proposed approach, we were ranked as the third best team in the LifeCLEF 2017 challenge.

  • 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/GBP103%2F12%2FG084" target="_blank" >GBP103/12/G084: Center for Large Scale Multi-modal Data Interpretation</a><br>

  • Continuities

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

Others

  • Publication year

    2017

  • Confidentiality

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

Data specific for result type

  • Article name in the collection

    Working Notes of CLEF 2017 - Conference and Labs of the Evaluation Forum

  • ISBN

  • ISSN

    1613-0073

  • e-ISSN

    1613-0073

  • Number of pages

    10

  • Pages from-to

  • Publisher name

    CEUR Workshop Proceedings

  • Place of publication

    Aachen

  • Event location

    Dublin

  • Event date

    Sep 11, 2017

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article