160 citations · 238 across the 11 of their papers we have counts for
10 papers · 1 filter
Cross-Modulation Networks for Few-Shot Learning
Hugo Prol, Vincent Dumoulin, Luis Herranz
A family of recent successful approaches to few-shot learning relies on learning an embedding space in which predictions are made by computing similarities between examples. This c…
Learning Effective RGB-D Representations for Scene Recognition
Xinhang Song, Shuqiang Jiang, Luis Herranz +1
Deep convolutional networks (CNN) can achieve impressive results on RGB scene recognition thanks to large datasets such as Places. In contrast, RGB-D scene recognition is still und…
Memory Replay GANs: learning to generate images from new categories without forgetting
Chenshen Wu, Luis Herranz, Xialei Liu +3
Previous works on sequential learning address the problem of forgetting in discriminative models. In this paper we consider the case of generative models. In particular, we investi…
LIUM-CVC Submissions for WMT18 Multimodal Translation Task
Ozan Caglayan, Adrien Bardet, Fethi Bougares +5
This paper describes the multimodal Neural Machine Translation systems developed by LIUM and CVC for WMT18 Shared Task on Multimodal Translation. This year we propose several modif…
Transferring GANs: generating images from limited data
Yaxing Wang, Chenshen Wu, Luis Herranz +3
Transferring the knowledge of pretrained networks to new domains by means of finetuning is a widely used practice for applications based on discriminative models. To the best of ou…
Mix and match networks: encoder-decoder alignment for zero-pair image translation
Yaxing Wang, Joost van de Weijer, Luis Herranz
We address the problem of image translation between domains or modalities for which no direct paired data is available (i.e. zero-pair translation). We propose mix and match networ…