AVILES-RIVERO, Angelica I.; PAPADAKIS, Nicolas; LI, Ruoteng; ALSALEH, Samar M; TAN, Robby T; SCHÖNLIEB, Carola-Bibiane

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AVILES-RIVERO, Angelica I.

PAPADAKIS, Nicolas
Institut de Mathématiques de Bordeaux [IMB]

LI, Ruoteng

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Document de travail - Pré-publication

Resumen en inglés

We consider the task of classifying when an extremely reduced amount of labelled data is available. This problem is of a great interest, in several real-world problems, as obtaining large amounts of labelled data is expensive and time consuming. We present a novel semi-supervised framework for multi-class classification that is based on the normalised and non-smooth graph 1-Laplacian. Our transductive framework is framed under a novel functional with carefully selected class priors - that enforces a sufficiently smooth solution that strengthens the intrinsic relation between the labelled and unlabelled data. We demonstrate through extensive experimental results on large datasets CIFAR-10 and ChestX-ray14, that our method outperforms classic methods and readily competes with recent deep-learning approaches.< Leer menos

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https://oskar-bordeaux.fr/handle/20.500.12278/192809

Proyecto europeo

Nonlocal Methods for Arbitrary Data Sources

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Importado de Hal

Centros de investigación

Institut de Mathématiques de Bordeaux (IMB) - UMR 5251