Tag: IJCV
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MineGAN++: Mining Generative Models for Efficient Knowledge Transfer to Limited Data Domains
Yaxing Wang, Abel Gonzalez-Garcia, Chenshen Wu, Luis Herranz, Fahad Shahbaz Khan, Shangling Jui, Joost van de Weijer Read Full Paper → GANs largely increases the potential impact of generative models. Therefore, we propose a novel knowledge transfer method for generative models based on mining the knowledge that is most beneficial to a specific target domain, either from a single or multiple […]
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IJCV paper on multi-modal I2I
Yaxing’s paper on ‘Mix and match networks: cross-modal alignment for zero-pair image-to-image translation’ has been accepted for publication in IJCV.
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Mix and match networks: cross-modal alignment for zero-pair image-to-image translation
Yaxing Wang, Luis Herranz, Joost van de Weijer Read Full Paper → This paper addresses the problem of inferring unseen cross-modal image-to-image translations between multiple modalities. We assume that only some of the pairwise translations have been seen (i.e. trained) and infer the remaining unseen translations (where training pairs are not available). We propose mix and match […]