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ComboGAN: Unrestrained Scalability for Image Domain Translation
Asha Anoosheh, Eirikur Agustsson, Radu Timofte +1
This year alone has seen unprecedented leaps in the area of learning-based image translation, namely CycleGAN, by Zhu et al. But experiments so far have been tailored to merely two…
Logo Synthesis and Manipulation with Clustered Generative Adversarial Networks
Alexander Sage, Eirikur Agustsson, Radu Timofte +1
Designing a logo for a new brand is a lengthy and tedious back-and-forth process between a designer and a client. In this paper we explore to what extent machine learning can solve…
Object Referring in Visual Scene with Spoken Language
Arun Balajee Vasudevan, Dengxin Dai, Luc Van Gool
Object referring has important applications, especially for human-machine interaction. While having received great attention, the task is mainly attacked with written language (tex…
Manifold-valued Image Generation with Wasserstein Generative Adversarial Nets
Zhiwu Huang, Jiqing Wu, Luc Van Gool
Generative modeling over natural images is one of the most fundamental machine learning problems. However, few modern generative models, including Wasserstein Generative Adversaria…
Wasserstein Divergence for GANs
Jiqing Wu, Zhiwu Huang, Janine Thoma +2
In many domains of computer vision, generative adversarial networks (GANs) have achieved great success, among which the family of Wasserstein GANs (WGANs) is considered to be state…
Detection-aided liver lesion segmentation using deep learning
Miriam Bellver, Kevis-Kokitsi Maninis, Jordi Pont-Tuset +3
A fully automatic technique for segmenting the liver and localizing its unhealthy tissues is a convenient tool in order to diagnose hepatic diseases and assess the response to the…