activity
20172026
most citedNeural Networks Regularization Through Class-wise Invariant Representation Learning

8 citations · 8 across the 3 of their papers we have counts for

collaborators

7 papers

cs.CV2026

Test Time Adaptation Methods for Point Cloud Registration in Laparoscopic Surgery

Nina Bodelot, Soufiane Belharbi, Eric Granger

3D point cloud registration in laparoscopic surgery estimates the transformation between an intraoperative organ reconstructed from video and its preoperative mesh. Because ground-…

cs.CV2020

Deep Active Learning for Joint Classification & Segmentation with Weak Annotator

Soufiane Belharbi, Ismail Ben Ayed, Luke McCaffrey +1

CNN visualization and interpretation methods, like class-activation maps (CAMs), are typically used to highlight the image regions linked to class predictions. These models allow t…

cs.CV2020

Deep Interpretable Classification and Weakly-Supervised Segmentation of Histology Images via Max-Min Uncertainty

Soufiane Belharbi, Jérôme Rony, Jose Dolz +3

Weakly-supervised learning (WSL) has recently triggered substantial interest as it mitigates the lack of pixel-wise annotations. Given global image labels, WSL methods yield pixel-…

cs.CV2019

Convolutional STN for Weakly Supervised Object Localization

Akhil Meethal, Marco Pedersoli, Soufiane Belharbi +1

Weakly supervised object localization is a challenging task in which the object of interest should be localized while learning its appearance. State-of-the-art methods recycle the…

cs.LG2019

Non-parametric Uni-modality Constraints for Deep Ordinal Classification

Soufiane Belharbi, Ismail Ben Ayed, Luke McCaffrey +1

We propose a new constrained-optimization formulation for deep ordinal classification, in which uni-modality of the label distribution is enforced implicitly via a set of inequalit…

cs.LG2018

Neural Networks Regularization Through Representation Learning

Soufiane Belharbi

Neural network models and deep models are one of the leading and state of the art models in machine learning. Most successful deep neural models are the ones with many layers which…