Abstract
Riccardo Volpi, Diane Larlus, Gregory Rogez |
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021, pp. 4443-4453. |
arXiv |
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Supplementary material |
@InProceedings{Volpi_2021_CVPR, author = {Volpi, Riccardo and Larlus, Diane and Rogez, Gregory}, title = {Continual Adaptation of Visual Representations via Domain Randomization and Meta-Learning}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2021}, pages = {4443-4453} }
Abstract
Most standard learning approaches lead to fragile models which are prone to drift when sequentially trained on samples of a different nature—the well-known catastrophic forgetting issue. In particular, when a model consecutively learns from different visual domains, it tends to forget the past ones in favor of the most recent. In this context, we show that one way to learn models that are inherently more robust against forgetting is domain randomization—for vision tasks, randomizing the current domain’s distribution with heavy image manipulations. Building on this result, we devise a meta-learning strategy where a regularizer explicitly penalizes any loss associated with transferring the model from the current domain to different “auxiliary” meta-domains, while also easing adaptation to them. Such meta-domains, are also generated through randomized image manipulations. We empirically demonstrate in a variety of experiments—spanning from classification to semantic segmentation—that our approach results in models that are less prone to catastrophic forgetting when transferred to new domains.
Details on the gender equality index score 2023 (related to year 2022) for NAVER France of 81/100.
NAVER France targets are as follows:
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Index NAVER France de l’égalité professionnelle entre les femmes et les hommes pour l’année 2023 au titre des données 2022 : 81/100
Détail des indicateurs :
Les objectifs de progression de NAVER France sont :
NAVER LABS Europe 6-8 chemin de Maupertuis 38240 Meylan France Contact
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