11 citations · 25 across the 3 of their papers we have counts for
6 papers
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…
Optimal transport maps for distribution preserving operations on latent spaces of Generative Models
Eirikur Agustsson, Alexander Sage, Radu Timofte +1
Generative models such as Variational Auto Encoders (VAEs) and Generative Adversarial Networks (GANs) are typically trained for a fixed prior distribution in the latent space, such…
Evaluating race and sex diversity in the world's largest companies using deep neural networks
Konstantin Chekanov, Polina Mamoshina, Roman V. Yampolskiy +3
Diversity is one of the fundamental properties for the survival of species, populations, and organizations. Recent advances in deep learning allow for the rapid and automatic asses…
Single Image Super Resolution - When Model Adaptation Matters
Yudong Liang, Radu Timofte, Jinjun Wang +2
In the recent years impressive advances were made for single image super-resolution. Deep learning is behind a big part of this success. Deep(er) architecture design and external p…
k2-means for fast and accurate large scale clustering
Eirikur Agustsson, Radu Timofte, Luc Van Gool
We propose k^2-means, a new clustering method which efficiently copes with large numbers of clusters and achieves low energy solutions. k^2-means builds upon the standard k-means (…
Fast Optical Flow using Dense Inverse Search
Till Kroeger, Radu Timofte, Dengxin Dai +1
Most recent works in optical flow extraction focus on the accuracy and neglect the time complexity. However, in real-life visual applications, such as tracking, activity detection…