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ESA☆: A generic framework for semi-supervised inductive learning

Abstract : Semi-supervised learning is crucial in many applications where accessing class labels is unaffordable or costly. The most promising approaches are graph-based but they are transductive and they do not provide a generalized model working on inductive scenarios. To address this problem, we propose a generic framework, ESA☆, for inductive semi-supervised learning based on three components: an ensemble of semi-supervised autoencoders providing a new data representation that leverages the knowledge supplied by the reduced amount of available labels; a graph-based step that helps augmenting the training set with pseudo-labeled instances and, finally, a classifier trained with labeled and pseudo-labeled instances. Additionally, we also introduce two variants of our framework adopting different graph-based pseudo-labeling strategies: the first, ESALP, is based on a confidence-aware label propagation algorithm, while the second, ESAGAT, on a graph convolutional attention network. The experimental results show that our framework outperforms state-of-the-art inductive semi-supervised methods.
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Contributor : Isabelle Nault <>
Submitted on : Wednesday, April 21, 2021 - 2:38:28 PM
Last modification on : Tuesday, June 15, 2021 - 2:57:36 PM

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Shuyi Yang, Dino Ienco, Roberto Esposito, Ruggero Pensa. ESA☆: A generic framework for semi-supervised inductive learning. Neurocomputing, Elsevier, 2021, 447, pp.102-117. ⟨10.1016/j.neucom.2021.03.051⟩. ⟨hal-03204391⟩



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