61 citations · 97 across the 20 of their papers we have counts for
7 papers · 1 filter
Transductive Few-Shot Learning: Clustering is All You Need?
Imtiaz Masud Ziko, Malik Boudiaf, Jose Dolz +2
We investigate a general formulation for clustering and transductive few-shot learning, which integrates prototype-based objectives, Laplacian regularization and supervision constr…
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…
Transductive Information Maximization For Few-Shot Learning
Malik Boudiaf, Ziko Imtiaz Masud, Jérôme Rony +3
We introduce Transductive Infomation Maximization (TIM) for few-shot learning. Our method maximizes the mutual information between the query features and their label predictions fo…
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…
Universal Adversarial Audio Perturbations
Sajjad Abdoli, Luiz G. Hafemann, Jerome Rony +3
We demonstrate the existence of universal adversarial perturbations, which can fool a family of audio classification architectures, for both targeted and untargeted attack scenario…
Variational Fair Clustering
Imtiaz Masud Ziko, Eric Granger, Jing Yuan +1
We propose a general variational framework of fair clustering, which integrates an original Kullback-Leibler (KL) fairness term with a large class of clustering objectives, includi…