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Graph Convolutional Networks for Learning with Few Clean and Many Noisy Labels

Conference paper
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Part of the Lecture Notes in Computer Science book series (LNCS, volume 12355)

Abstract

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 GCN-based 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.

Notes

Acknowledgements

This work is funded by MSMT LL1901 ERC-CZ grant and OP VVV funded project CZ.02.1.01/0.0/0.0/16_019/0000765 “Research Center for Informatics”.

Supplementary material

504449_1_En_17_MOESM1_ESM.pdf (315 kb)
Supplementary material 1 (pdf 315 KB)

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Google ResearchMeylanFrance
  2. 2.VRG, Faculty of Electrical EngineeringCzech Technical University in PraguePragueCzech Republic
  3. 3.Inria, Univ Rennes, CNRS, IRISARennesFrance

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