25 citations · 65 across the 23 of their papers we have counts for
5 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…
Joint Progressive Knowledge Distillation and Unsupervised Domain Adaptation
Le Thanh Nguyen-Meidine, Eric Granger, Madhu Kiran +2
Currently, the divergence in distributions of design and operational data, and large computational complexity are limiting factors in the adoption of CNNs in real-world application…
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…
Progressive Gradient Pruning for Classification, Detection and DomainAdaptation
Le Thanh Nguyen-Meidine, Eric Granger, Madhu Kiran +2
Although deep neural networks (NNs) have achievedstate-of-the-art accuracy in many visual recognition tasks,the growing computational complexity and energy con-sumption of networks…