1 citations · 1 across the 11 of their papers we have counts for
12 papers
SOL: Measuring Gaps between Text Distributions by Double Sliced Wasserstein Metrics
Gregor Kornhardt, Moritz Piening, Jannis Chemseddine +1
Evaluating text generation requires measuring how well the generated distribution matches the data distribution. For autoregressive models, this is done by the perplexity. Diffusio…
Alignment Matters Inside and Out in Equivariant Graph Flow Matching
Moritz Piening, Christian Wald
Graphs are invariant under node permutations, motivating permutation-equivariant architectures in generative models. In flow matching, however, symmetry also affects the source-tar…
Generalized Wasserstein Flow Matching: Nested Couplings for Point Cloud Generation
Moritz Piening, Richard Duong, Gabriele Steidl
We formulate flow matching on probability measures over probability measures. Directly from the Wasserstein-on-Wasserstein (WoW) continuity equation, we show that admissible nested…
HOT-POT: Optimal Transport for Sparse Stereo Matching
Antonin Clerc, Michael Quellmalz, Moritz Piening +3
Stereo vision between images faces a range of challenges, including occlusions, motion, and camera distortions, across applications in autonomous driving, robotics, and face analys…
Slicing Wasserstein Over Wasserstein Via Functional Optimal Transport
Moritz Piening, Robert Beinert
Wasserstein distances define a metric between probability measures on arbitrary metric spaces, including meta-measures (measures over measures). The resulting Wasserstein over Wass…
A Novel Sliced Fused Gromov-Wasserstein Distance
Moritz Piening, Robert Beinert
The Gromov--Wasserstein (GW) distance and its fused extension (FGW) are powerful tools for comparing heterogeneous data. Their computation is, however, challenging since both dista…
Slicing the Gaussian Mixture Wasserstein Distance
Moritz Piening, Robert Beinert
Gaussian mixture models (GMMs) are widely used in machine learning for tasks such as clustering, classification, image reconstruction, and generative modeling. A key challenge in w…
Joint Metric Space Embedding by Unbalanced OT with Gromov-Wasserstein Marginal Penalization
Florian Beier, Moritz Piening, Robert Beinert +1
We propose a new approach for unsupervised alignment of heterogeneous datasets, which maps data from two different domains without any known correspondences to a common metric spac…
Paired Wasserstein Autoencoders for Conditional Sampling
Moritz Piening, Matthias Chung
Generative autoencoders learn compact latent representations of data distributions through jointly optimized encoder--decoder pairs. In particular, Wasserstein autoencoders (WAEs)…
Conditional Generative Models for Contrast-Enhanced Synthesis of T1w and T1 Maps in Brain MRI
Moritz Piening, Fabian Altekrüger, Gabriele Steidl +2
Contrast enhancement by Gadolinium-based contrast agents (GBCAs) is a vital tool for tumor diagnosis in neuroradiology. Based on brain MRI scans of glioblastoma before and after Ga…
T-FAKE: Synthesizing Thermal Images for Facial Landmarking
Philipp Flotho, Moritz Piening, Anna Kukleva +1
Facial analysis is a key component in a wide range of applications such as healthcare, autonomous driving, and entertainment. Despite the availability of various facial RGB dataset…
Learning from small data sets: Patch-based regularizers in inverse problems for image reconstruction
Moritz Piening, Fabian Altekrüger, Johannes Hertrich +3
The solution of inverse problems is of fundamental interest in medical and astronomical imaging, geophysics as well as engineering and life sciences. Recent advances were made by u…