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
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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
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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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
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ISSN
1613-0073
e-ISSN
1613-0073
Number of pages
10
Pages from-to
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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
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