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In this work we consider the problem of learning a classifier from noisylabels when a few clean labeled examples are given. The structure of clean andnoisy 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 cleanfrom noisy examples using a weighted binary cross-entropy loss function. TheGCN-inferred "clean" probability is then exploited as a relevance measure. Eachnoisy example is weighted by its relevance when learning a classifier for theend task. We evaluate our method on an extended version of a few-shot learningproblem, where the few clean examples of novel classes are supplemented withadditional noisy data. Experimental results show that our GCN-based cleaningprocess significantly improves the classification accuracy over not cleaningthe noisy data, as well as standard few-shot classification where only fewclean examples are used.

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