SCHMUTZ , Hugo

Semi supervised learning (SSL) provides an effective means of leveraging unlabelled data to improve a model's performance. Even though the domain has received a considerable amount of attention in the past years, most methods present the common drawback of being unsafe. By safeness we mean the quality of not degrading a fully supervised model...

We present simple methods for out-of-distribution detection using a trained generative model. These techniques, based on classical statistical tests, are model-agnostic in the sense that they can be applied to any differentiable generative model. The idea is to combine a classical parametric test (Rao's score test) with the recently introduced...

International audience

Semi-supervised learning is a powerful techniquefor leveraging unlabeled data to improve machinelearning models, but it can be affected by the pres-ence of "informative" labels, which occur whensome classes are more likely to be labeled thanothers. In the missing data literature, such labelsare called missing not at random. In this paper,we...

Abstract Purpose FDOPA PET shows good performance for the diagnosis of striatal dopaminergic denervation, making it a valuable tool for the differential diagnosis of Parkinsonism. Textural features are image biomarkers that could potentially improve the early diagnosis and monitoring of neurodegenerative parkinsonian syndromes. We explored the...