Hallucinating a Cleanly Labeled Augmented Dataset from a Noisy Labeled Dataset Using GANs

F. Chiaroni (1, 2) , M-C. Rahal (1) , N. Hueber (3) , Frédéric Dufaux (2)

1 Institut VEDECOM
2 L2S - Laboratoire des signaux et systèmes
3 ISL - Institut franco-allemand de recherches de Saint-Louis

Noisy labeled learning methods deal with training datasets containing corrupted labels. However, prediction performances of existing methods on small datasets still leave room for improvements. With this objective, in this paper we present a GAN-based method to generate a clean augmented training dataset from a small and noisy labeled dataset. The proposed approach combines noisy labeled learning principles with GAN state-of-the-art techniques. We demonstrate the usefulness of the proposed approach through an empirical study on simple and complex image datasets.

Mots clés: generative adversarial networks; image classification; noisy labeled learning

Domaines: Apprentissage [cs.LG]; Réseau de neurones [cs.NE]; Vision par ordinateur et reconnaissance de formes [cs.CV]; Intelligence artificielle [cs.AI]; Traitement du signal et de l'image [eess.SP]

F. Chiaroni, M-C. Rahal, N. Hueber, Frédéric Dufaux. Hallucinating a Cleanly Labeled Augmented Dataset from a Noisy Labeled Dataset Using GANs. 26th IEEE International Conference on Image Processing (ICIP 2019), Sep 2019, Taipei, Taiwan. pp.3616-3620, ⟨10.1109/ICIP.2019.8803632⟩. ⟨hal-02054836⟩