8 citations · 24 across the 28 of their papers we have counts for
4 papers · 1 filter
Informative GANs via Structured Regularization of Optimal Transport
Pierre Bréchet, Tao Wu, Thomas Möllenhoff +1
We tackle the challenge of disentangled representation learning in generative adversarial networks (GANs) from the perspective of regularized optimal transport (OT). Specifically,…
Flat Metric Minimization with Applications in Generative Modeling
Thomas Möllenhoff, Daniel Cremers
We take the novel perspective to view data not as a probability distribution but rather as a current. Primarily studied in the field of geometric measure theory, -currents are c…
Lifting Vectorial Variational Problems: A Natural Formulation based on Geometric Measure Theory and Discrete Exterior Calculus
Thomas Möllenhoff, Daniel Cremers
Numerous tasks in imaging and vision can be formulated as variational problems over vector-valued maps. We approach the relaxation and convexification of such vectorial variational…
Controlling Neural Networks via Energy Dissipation
Michael Moeller, Thomas Möllenhoff, Daniel Cremers
The last decade has shown a tremendous success in solving various computer vision problems with the help of deep learning techniques. Lately, many works have demonstrated that lear…