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

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.