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Semi Few-Shot Attribute Translation

  • Recent studies have shown remarkable success in image-to-image translation for attribute transfer applications. However, most of existing approaches are based on deep learning and require an abundant amount of labeled data to produce good results, therefore limiting their applicability. In the same vein, recent advances in meta-learning have led to successful implementations with limited availableRecent studies have shown remarkable success in image-to-image translation for attribute transfer applications. However, most of existing approaches are based on deep learning and require an abundant amount of labeled data to produce good results, therefore limiting their applicability. In the same vein, recent advances in meta-learning have led to successful implementations with limited available data, allowing so-called few-shot learning. In this paper, we address this limitation of supervised methods, by proposing a novel approach based on GANs. These are trained in a meta-training manner, which allows them to perform image-to-image translations using just a few labeled samples from a new target class. This work empirically demonstrates the potential of training a GAN for few shot image-to-image translation on hair color attribute synthesis tasks, opening the door to further research on generative transfer learning.show moreshow less

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Metadaten
Author:Ricard Durall Lopez, Franz-Josef Pfreundt, Janis KeuperORCiDGND
Publisher:arXiv e-print
Year of Publication:2019
Pagenumber:9
Language:English
DDC classes:000 Allgemeines, Informatik, Informationswissenschaft
Document Type:Article (unreviewed)
Open Access:Frei zugänglich
Institutes:Bibliografie
Release Date:2020/01/21
Licence (German):License LogoEs gilt das UrhG
ArXiv Id:http://arxiv.org/abs/1910.03240