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We introduce a novel distillation technique for ResShift, one of the best diffusion models for real-world image super-resolution.

Interaction Field Matching (IFM) generalizes Electrostatic Field Matching (EFM) by employing general interaction fields, including a quark-inspired solution to modeling challenges of EFM.

iJKOnet combines the JKO scheme with inverse optimization to learn population dynamics without restrictive architectural constraints, achieving better performance than previous JKO-based methods.

We provide theoretical generalization error bounds for neural network-based Optimal Transport (OT) solvers using minimax approaches, focusing on quadratic OT, and show these bounds depend on standard properties of neural networks, offering a foundation for extending analysis to broader OT formulations.

We introduce a novel mutual information (MI) estimator leveraging diffusion bridge models, which provides unbiased estimates for challenging data and demonstrates strong performance on standard MI estimation benchmarks.

We propose a novel light learning algorithm for semi-supervised domain translation which can utilize both paired and unpaired data.

We discover that the existing heuristical bi-directional implementation of Iterative Markovian fitting (IMF) procedure for the Schrödinger Bridge problem secretly utilizes the other well-celebrated Iterative Proportional fitting procedure. We analyze such a cross-over of two procedures both theoretically and practically.

We reveal several limitations of existing solvers for the continuous Gromov-Wasserstein problem and pave the way to get rid of them by proposing a novel solver.