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.
1. Difference in female/male salary: 33/40 points
2. Difference in salary increases female/male: 35/35 points
3. Salary increases upon return from maternity leave: uncalculable
4. Number of employees in under-represented gender in 10 highest salaries: 0/10 points
NAVER France targets (with respect to the 2022 index) are as follows:
En 2022, NAVER France a obtenu les notes suivantes pour chacun des indicateurs :
1. Les écarts de salaire entre les femmes et les hommes: 33 sur 40 points
2. Les écarts des augmentations individuelles entre les femmes et les hommes : 35 sur 35 points
3. Toutes les salariées augmentées revenant de congé maternité : non calculable
4. Le nombre de salarié du sexe sous-représenté parmi les 10 plus hautes rémunérations : 0 sur 10 points
Les objectifs de progression pour l’index 2022 de NAVER France sont :
NAVER LABS Europe 6-8 chemin de Maupertuis 38240 Meylan France Contact
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