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

Ahmet Iscen 1 Giorgos Tolias 2 Yannis Avrithis 1 Ondřej Chum 2 Cordelia Schmid 3
1 LinkMedia - Creating and exploiting explicit links between multimedia fragments
Inria Rennes – Bretagne Atlantique , IRISA-D6 - MEDIA ET INTERACTIONS
2 VRG - Visual Recognition Group [Prague]
CTU/FEE - Faculty of electrical engineering [Prague]
3 Thoth - Apprentissage de modèles à partir de données massives
LJK - Laboratoire Jean Kuntzmann , Inria Grenoble - Rhône-Alpes
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 learning to discriminate clean from noisy examples using a weighted binary cross-entropy loss function, and then the GCN-inferred "clean" probability is 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 and standard few-shot classification where only few clean examples are used. The proposed GCN-based method outperforms the transductive approach (Douze et al., 2018) that is using the same additional data without labels.
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https://hal.inria.fr/hal-02370212
Contributor : Yannis Avrithis <>
Submitted on : Tuesday, November 19, 2019 - 12:54:42 PM
Last modification on : Thursday, November 19, 2020 - 1:01:31 PM
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  • HAL Id : hal-02370212, version 1
  • ARXIV : 1910.00324

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Ahmet Iscen, Giorgos Tolias, Yannis Avrithis, Ondřej Chum, Cordelia Schmid. Graph Convolutional Networks for Learning with Few Clean and many Noisy Labels. 2019. ⟨hal-02370212⟩

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