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Communication Dans Un Congrès Année : 2024

CONTRASTIVE LEARNING FOR REGRESSION ON HYPERSPECTRAL DATA

Paul Honeine
Mohamad Dhaini
Maxime Berar
Antonin van Exem
  • Fonction : Auteur

Résumé

Contrastive learning has demonstrated great effectiveness in representation learning especially for image classification tasks. However, there is still a shortage in the studies targeting regression tasks, and more specifically applications on hyperspectral data. In this paper, we propose a contrastive learning framework for the regression tasks for hyperspectral data. To this end, we provide a collection of transformations relevant for augmenting hyperspectral data, and investigate contrastive learning for regression. Experiments on synthetic and real hyperspectral datasets show that the proposed framework and transformations significantly improve the performance of regression models, achieving better scores than other state-of-the-art transformations.
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Dates et versions

hal-04360616 , version 1 (21-12-2023)

Identifiants

  • HAL Id : hal-04360616 , version 1

Citer

Paul Honeine, Mohamad Dhaini, Maxime Berar, Antonin van Exem. CONTRASTIVE LEARNING FOR REGRESSION ON HYPERSPECTRAL DATA. Proceedings of the 49th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, Apr 2024, Seoul (Korea), South Korea. ⟨hal-04360616⟩
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