18 citations · 35 across the 12 of their papers we have counts for
6 papers · 1 filter
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…
In Search for a Generalizable Method for Source Free Domain Adaptation
Malik Boudiaf, Tom Denton, Bart van Merriënboer +2
Source-free domain adaptation (SFDA) is compelling because it allows adapting an off-the-shelf model to a new domain using only unlabelled data. In this work, we apply existing SFD…
Towards Practical Few-Shot Query Sets: Transductive Minimum Description Length Inference
Ségolène Martin, Malik Boudiaf, Emilie Chouzenoux +2
Standard few-shot benchmarks are often built upon simplifying assumptions on the query sets, which may not always hold in practice. In particular, for each task at testing time, th…
Realistic Evaluation of Transductive Few-Shot Learning
Olivier Veilleux, Malik Boudiaf, Pablo Piantanida +1
Transductive inference is widely used in few-shot learning, as it leverages the statistics of the unlabeled query set of a few-shot task, typically yielding substantially better pe…
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…
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…