61 citations · 174 across the 62 of their papers we have counts for
6 papers · 1 filter
Temporal Stochastic Softmax for 3D CNNs: An Application in Facial Expression Recognition
Théo Ayral, Marco Pedersoli, Simon Bacon +1
Training deep learning models for accurate spatiotemporal recognition of facial expressions in videos requires significant computational resources. For practical reasons, 3D Convol…
Augmented Lagrangian Adversarial Attacks
Jérôme Rony, Eric Granger, Marco Pedersoli +1
Adversarial attack algorithms are dominated by penalty methods, which are slow in practice, or more efficient distance-customized methods, which are heavily tailored to the propert…
Learning Data Augmentation with Online Bilevel Optimization for Image Classification
Saypraseuth Mounsaveng, Issam Laradji, Ismail Ben Ayed +2
Data augmentation is a key practice in machine learning for improving generalization performance. However, finding the best data augmentation hyperparameters requires domain knowle…
Semi-supervised few-shot learning for medical image segmentation
Abdur R Feyjie, Reza Azad, Marco Pedersoli +3
Recent years have witnessed the great progress of deep neural networks on semantic segmentation, particularly in medical imaging. Nevertheless, training high-performing models requ…
A unifying mutual information view of metric learning: cross-entropy vs. pairwise losses
Malik Boudiaf, Jérôme Rony, Imtiaz Masud Ziko +4
Recently, substantial research efforts in Deep Metric Learning (DML) focused on designing complex pairwise-distance losses, which require convoluted schemes to ease optimization, s…
On the Texture Bias for Few-Shot CNN Segmentation
Reza Azad, Abdur R Fayjie, Claude Kauffman +3
Despite the initial belief that Convolutional Neural Networks (CNNs) are driven by shapes to perform visual recognition tasks, recent evidence suggests that texture bias in CNNs pr…