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Optimal Transport as a Defense Against Adversarial Attacks
Published:
We present Sinkhorn Adversarial Training (SAT), a robust adversarial training method based on the latest theory of optimal transport. We also propose a new metric, the Area Under Accuracy Curve (AUAC), to quantify more precisely the robustness of a model to adversarial attacks over a wide range of perturbation sizes.
Vulnerability of Person Re-Identification Models to Metric Adversarial Attacks
Published:
We investigate different possible attacks on metric learning models depending on the number and type of guides available. Two particularly effective attacks stand out. To defend against these attacks, we adapt the adversarial training protocol for metric learning. Let us guide you !
portfolio
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publications
Vulnerability of Person Re-Identification Models to Metric Adversarial Attacks.
Published in CVPRW, 2020
Recommended citation: "Vulnerability of Person Re-Identification Models to Metric Adversarial Attacks." Quentin Bouniot, Romaric Audigier, Angélique Loesch (2020). IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops.
Towards Better Understanding Meta-learning Methods through Multi-task Representation Learning Theory.
Published in arXiv, 2020
Recommended citation: "Towards Better Understanding Meta-learning Methods through Multi-task Representation Learning Theory." Quentin Bouniot, Ievgen Redko, Romaric Audigier, Angélique Loesch, Yevhenii Zotkin, Amaury Habrard (2020). arXiv preprint.
Optimal Transport as a Defense Against Adversarial Attacks
Published in ICPR 2020, 2021
Recommended citation: "Optimal Transport as a Defense Against Adversarial Attacks." Quentin Bouniot, Romaric Audigier, Angélique Loesch (2020). IEEE International Conference on Pattern Recognition (ICPR) 2020.
talks
Vulnerability of Person Re-Identification Models to Metric Adversarial Attacks
Published:
We investigate different possible attacks on metric learning models depending on the number and type of guides available. Two particularly effective attacks stand out. To defend against these attacks, we adapt the adversarial training protocol for metric learning. Let us guide you !
Vulnerability of Person Re-Identification Models to Metric Adversarial Attacks
Published:
We investigate different possible attacks on metric learning models depending on the number and type of guides available. Two particularly effective attacks stand out. To defend against these attacks, we adapt the adversarial training protocol for metric learning. Let us guide you !
Putting Theory to Work: From Learning Bounds to Meta-Learning Algorithms
Published:
In this work, we want to apply the latest insights from meta-learning theory in practice to ensure a good meta-learning.
Optimal Transport as a Defense Against Adversarial Attacks
Published:
We present Sinkhorn Adversarial Training (SAT), a robust adversarial training method based on the latest theory of optimal transport. We also propose a new metric, the Area Under Accuracy Curve (AUAC), to quantify more precisely the robustness of a model to adversarial attacks over a wide range of perturbation sizes.
Vers une meilleure compréhension des méthodes de méta-apprentissage à travers la théorie de l’apprentissage de représentations multi-tâches
Published:
Dans nos travaux nous avons cherché à faire le lien entre le meta-learning et l’apprentissage de représentation multi-tâche, qui possède une importante littérature théorique ainsi que des bornes d’apprentissage solides. Et en analysant les bornes les plus récentes d’apprentissage de représentation multi-tâches et leurs hypothèses, nous avons mis en évidence des critères qui permettent un méta-apprentissage plus efficace.
Improving Few-Shot Learning through Multi-task Representation Learning Theory
Published:
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. We start by reviewing recent advances in MTR theory and show that they can provide novel insights for popular meta-learning algorithms when analyzed within this framework. In particular, we highlight a fundamental difference between gradient-based and metric-based algorithms and put forward a theoretical analysis to explain it. Finally, we use the derived insights to improve the generalization capacity of meta-learning methods via a new spectral-based regularization term and confirm its efficiency through experimental studies on classic few-shot classification benchmarks. To the best of our knowledge, this is the first contribution that puts the most recent learning bounds of MTR theory into practice for the task of few-shot classification
teaching
Algorithms and complexity
Course Lecturer, CentraleSupélec, Université Paris-Saclay, 1900
First year Computer Science course for the main engineering track at CentraleSupélec
tips
Bash
Published:
Useful commands for bash.
Conda
Published:
Manage Python environments with conda.
Pytorch
Published:
Basic notions of Pytorch and useful functions to manipulate tensors.
Computation time
Published:
A trick to improve computation time when working with lists.
f-string
Published:
Clean and efficient string formatting in Python >3.6
Pathlib
Published:
Easy path handling in Python >3.4
Tqdm
Published:
Beautiful progress bars for loops in Python
Einsum
Published:
Einstein Summation in Numpy of Pytorch
Kubernetes
Published:
Basic notions for kubernetes
Git
Published:
Basic notions of Git. Branching, Merging and Stashing.
Regex
Published:
Basic Notions of Regex