This paper considers the problem of positive unlabeled (PU) learning. In this context, we propose a two-stage GAN-based model.

Oct 4, 2019 · Abstract page for arXiv paper 1910.01968: Generating Relevant Counter-Examples from a Positive Unlabeled Dataset for Image Classification.

This paper considers the problem of positive unlabeled (PU) learning. In this context, we propose a two-stage GAN-based model. More specifically, the main ...

This study proposes a novel sampling approach called single-to-multi-label (S2M) sampling, based on the Markov chain Monte Carlo method, which enables ...

This work deals with this demanding task for which recent GAN-based PU approaches have demonstrated promising results. Generative adversarial Networks (GANs) ...

Sep 27, 2018 · Under the assumption that unlabeled data contain relevant counter-examples, using a PU learning method enables to focus the training dataset ...

Highlights：. • It is possible to constrain a Generative Adversarial Network (GAN) in order to capture the counter-examples distribution from a Positive ...

We theoretically elicit the loss function from the setting of PU learning. Counter-examples generation from a positive unlabeled image dataset.[paper].

Learning with a generative adversarial network from a positive unlabeled dataset for image ... Counter-examples generation from a positive unlabeled image dataset.

Generating Relevant Counter-Examples from a Positive Unlabeled Dataset for Image Classification · 1 code implementation • 4 Oct 2019 • Florent Chiaroni ...