61 citations · 174 across the 62 of their papers we have counts for
13 papers · 1 filter
LT-Soups: Bridging Head and Tail Classes via Subsampled Model Soups
Masih Aminbeidokhti, Subhankar Roy, Eric Granger +2
Real-world datasets typically exhibit long-tailed (LT) distributions, where a few head classes dominate and many tail classes are severely underrepresented. While recent work shows…
High-Rate Mixout: Revisiting Mixout for Robust Domain Generalization
Masih Aminbeidokhti, Heitor Rapela Medeiros, Srikanth Muralidharan +2
Ensembling fine-tuned models initialized from powerful pre-trained weights is a common strategy to improve robustness under distribution shifts, but it comes with substantial compu…
Robust and Efficient Finetuning of Vision Foundation Models via Implicit Ensembling
Masih Aminbeidokhti, Heitor Rapela Medeiros, Eric Granger +1
Robust finetuning of foundation models seeks to improve out-of-distribution (OOD) generalization during in-distribution (ID) adaptation. We revisit Mixout, a stochastic regularizer…
Learning from Stochastic Teacher Representations Using Student-Guided Knowledge Distillation
Muhammad Haseeb Aslam, Clara Martinez, Marco Pedersoli +3
Advances in self-distillation have shown that when knowledge is distilled from a teacher to a student using the same deep learning (DL) architecture, the student performance can su…
BigDocs: An Open Dataset for Training Multimodal Models on Document and Code Tasks
Juan Rodriguez, Xiangru Jian, Siba Smarak Panigrahi +40
Multimodal AI has the potential to significantly enhance document-understanding tasks, such as processing receipts, understanding workflows, extracting data from documents, and sum…
Do not trust what you trust: Miscalibration in Semi-supervised Learning
Shambhavi Mishra, Balamurali Murugesan, Ismail Ben Ayed +2
State-of-the-art semi-supervised learning (SSL) approaches rely on highly confident predictions to serve as pseudo-labels that guide the training on unlabeled samples. An inherent…
Bag of Tricks for Fully Test-Time Adaptation
Saypraseuth Mounsaveng, Florent Chiaroni, Malik Boudiaf +2
Fully Test-Time Adaptation (TTA), which aims at adapting models to data drifts, has recently attracted wide interest. Numerous tricks and techniques have been proposed to ensure ro…
Domain Generalization by Rejecting Extreme Augmentations
Masih Aminbeidokhti, Fidel A. Guerrero Peña, Heitor Rapela Medeiros +3
Data augmentation is one of the most effective techniques for regularizing deep learning models and improving their recognition performance in a variety of tasks and domains. Howev…
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…
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…
Emotion Recognition with Spatial Attention and Temporal Softmax Pooling
Masih Aminbeidokhti, Marco Pedersoli, Patrick Cardinal +1
Video-based emotion recognition is a challenging task because it requires to distinguish the small deformations of the human face that represent emotions, while being invariant to…
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…
Deep clustering: On the link between discriminative models and K-means
Mohammed Jabi, Marco Pedersoli, Amar Mitiche +1
In the context of recent deep clustering studies, discriminative models dominate the literature and report the most competitive performances. These models learn a deep discriminati…