Graph convolutional networks for learning with few clean and many noisy labels

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F20%3A00345902" target="_blank" >RIV/68407700:21230/20:00345902 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1007/978-3-030-58607-2_17" target="_blank" >https://doi.org/10.1007/978-3-030-58607-2_17</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-030-58607-2_17" target="_blank" >10.1007/978-3-030-58607-2_17</a>

Result language
angličtina
Original language name
Graph convolutional networks for learning with few clean and many noisy labels
Original language description
In this work we consider the problem of learning a classifier from noisy labels when a few clean labeled examples are given. The structure of clean and noisy data is modeled by a graph per class and Graph Convolutional Networks (GCN) are used to predict class relevance of noisy examples. For each class, the GCN is treated as a binary classifier, which learns to discriminate clean from noisy examples using a weighted binary cross-entropy loss function. The GCN-inferred “clean” probability is then exploited as a relevance measure. Each noisy example is weighted by its relevance when learning a classifier for the end task. We evaluate our method on an extended version of a few-shot learning problem, where the few clean examples of novel classes are supplemented with additional noisy data. Experimental results show that our GCNbased cleaning process significantly improves the classification accuracy over not cleaning the noisy data, as well as standard few-shot classification where only few clean examples are used.
Czech name
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Czech description
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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)

Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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