61 citations · 97 across the 23 of their papers we have counts for
4 papers · 1 filter
Towards Practical Few-Shot Query Sets: Transductive Minimum Description Length Inference
Ségolène Martin, Malik Boudiaf, Emilie Chouzenoux +2
Standard few-shot benchmarks are often built upon simplifying assumptions on the query sets, which may not always hold in practice. In particular, for each task at testing time, th…
TTTFlow: Unsupervised Test-Time Training with Normalizing Flow
David Osowiechi, Gustavo A. Vargas Hakim, Mehrdad Noori +3
A major problem of deep neural networks for image classification is their vulnerability to domain changes at test-time. Recent methods have proposed to address this problem with te…
Test-Time Adaptation with Shape Moments for Image Segmentation
Mathilde Bateson, Hervé Lombaert, Ismail Ben Ayed
Supervised learning is well-known to fail at generalization under distribution shifts. In typical clinical settings, the source data is inaccessible and the target distribution is…
Leveraging Uncertainty for Deep Interpretable Classification and Weakly-Supervised Segmentation of Histology Images
Soufiane Belharbi, Jérôme Rony, Jose Dolz +3
Trained using only image class label, deep weakly supervised methods allow image classification and ROI segmentation for interpretability. Despite their success on natural images,…