1 code implementation • 30 Oct 2023 • Quentin Bouniot, Angélique Loesch, Romaric Audigier, Amaury Habrard
For specialized and dense downstream tasks such as object detection, labeling data requires expertise and can be very expensive, making few-shot and semi-supervised models much more attractive alternatives.
no code implementations • 25 Oct 2023 • Quentin Bouniot, Romaric Audigier, Angélique Loesch, Amaury Habrard
However, for unsupervised pretraining, the popular contrastive learning requires a large batch size and, therefore, a lot of resources.
no code implementations • 27 Oct 2022 • Yassine Naji, Aleksandr Setkov, Angélique Loesch, Michèle Gouiffès, Romaric Audigier
Abnormal event detection in videos is a challenging problem, partly due to the multiplicity of abnormal patterns and the lack of their corresponding annotations.
Ranked #2 on Anomaly Detection on UCSD Ped2
no code implementations • 7 Mar 2022 • Khalil Bergaoui, Yassine Naji, Aleksandr Setkov, Angélique Loesch, Michèle Gouiffès, Romaric Audigier
This paper addresses video anomaly detection problem for videosurveillance.
Ranked #3 on Anomaly Detection on UCSD Peds2
no code implementations • 24 Dec 2021 • Fabian Dubourvieux, Romaric Audigier, Angélique Loesch, Samia Ainouz, Stéphane Canu
(ii) General good practices for Pseudo-Labeling, directly deduced from the interpretation of the proposed theoretical framework, in order to improve the target re-ID performance.
no code implementations • 15 Oct 2021 • Fabian Dubourvieux, Angélique Loesch, Romaric Audigier, Samia Ainouz, Stéphane Canu
However, the effectiveness of these approaches heavily depends on the choice of some hyperparameters (HP) that affect the generation of pseudo-labels by clustering.
1 code implementation • 5 Feb 2021 • Quentin Bouniot, Romaric Audigier, Angélique Loesch
This leads to SAT (Sinkhorn Adversarial Training), a more robust defense against adversarial attacks.
1 code implementation • 5 Oct 2020 • Quentin Bouniot, Ievgen Redko, Romaric Audigier, Angélique Loesch, Amaury Habrard
In this paper, we consider the framework of multi-task representation (MTR) learning where the goal is to use source tasks to learn a representation that reduces the sample complexity of solving a target task.
no code implementations • 28 Sep 2020 • Quentin Bouniot, Ievgen Redko, Romaric Audigier, Angélique Loesch, Amaury Habrard
To the best of our knowledge, this is the first contribution that puts the most recent learning bounds of meta-learning theory into practice for the popular task of few-shot classification.