| Vulnerability of person re-identification models to metric adversarial attacks Q Bouniot, R Audigier, A Loesch Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 33 | 2020 |
| Improving few-shot learning through multi-task representation learning theory Q Bouniot, I Redko, R Audigier, A Loesch, A Habrard European conference on computer vision, 435-452, 2022 | 10 | 2022 |
| Optimal transport as a defense against adversarial attacks Q Bouniot, R Audigier, A Loesch 2020 25th International Conference on Pattern Recognition (ICPR), 5044-5051, 2021 | 9 | 2021 |
| Towards few-annotation learning for object detection: Are transformer-based models more efficient? Q Bouniot, A Loesch, R Audigier, A Habrard Proceedings of the IEEE/CVF winter conference on applications of computer …, 2023 | 6 | 2023 |
| Towards better understanding meta-learning methods through multi-task representation learning theory Q Bouniot, I Redko, R Audigier, A Loesch, Y Zotkin, A Habrard arXiv preprint arXiv:2010.01992, 2020 | 5 | 2020 |
| Tailoring Mixup to Data for Calibration Q Bouniot, P Mozharovskyi, F d'Alché-Buc arXiv preprint arXiv:2311.01434, 2023 | 3 | 2023 |
| Proposal-contrastive pretraining for object detection from fewer data Q Bouniot, R Audigier, A Loesch, A Habrard arXiv preprint arXiv:2310.16835, 2023 | 3 | 2023 |
| From alexnet to transformers: Measuring the non-linearity of deep neural networks with affine optimal transport Q Bouniot, I Redko, A Mallasto, C Laclau, K Arndt, O Struckmeier, ... arXiv preprint arXiv:2310.11439, 2023 | 3 | 2023 |
| The robust semantic segmentation uncv2023 challenge results X Yu, Y Zuo, Z Wang, X Zhang, J Zhao, Y Yang, L Jiao, R Peng, X Wang, ... Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2023 | 3 | 2023 |
| Towards Few-Annotation Learning in Computer Vision: Application to Image Classification and Object Detection tasks Q Bouniot arXiv preprint arXiv:2311.04888, 2023 | 1 | 2023 |
| Restyling Unsupervised Concept Based Interpretable Networks with Generative Models J Parekh, Q Bouniot, P Mozharovskyi, A Newson, F d'Alché-Buc arXiv preprint arXiv:2407.01331, 2024 | | 2024 |
| Understanding deep neural networks through the lens of their non-linearity Q Bouniot, I Redko, A Mallasto, C Laclau, O Struckmeier, K Arndt, ... | | 2023 |
| Understanding Few-Shot Multi-Task Representation Learning Theory Q Bouniot, I Redko ICLR Blog Track, 2022 | | 2022 |
| Vers une meilleure compréhension des méthodes de méta-apprentissage à travers la théorie de l’apprentissage de représentations multi-tâches Q Bouniot, I Redko, R Audigier, A Loesch | | 2021 |
| Putting theory to work: from learning bounds to meta-learning algorithms Q Bouniot, I Redko, R Audigier, A Loesch, A Habrard Workshop Meta-Learn@ NeurIPS 2020, 2020 | | 2020 |
| Tailoring Mixup to Data for Calibration Q Bouniot, P Mozharovskyi, F d'Alché-Buc The Thirteenth International Conference on Learning Representations, 0 | | |
| Conceptualize Any Network: A Concept Extraction Framework for Holistic Interpretability of Image Classifiers H Moghaddam, Q Bouniot, J Parekh, P Mozharovskyi, F d'Alché-Buc | | |
Angelique LoeschResearch Engineer, CEA LISTVerifierad e-postadress på cea.fr