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Diffusion and flow-matching models achieve remarkable generative performance but at the cost of many sampling steps, this slows inference and limits applicability to time-critical tasks.
The hungarian method for the assignment problem
H. W. Kuhn · 1955
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Herbert Robbins · 1956
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A Relationship Between Arbitrary Positive Matrices and Doubly Stochastic Matrices
Richard Sinkhorn · 1964
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Iterative procedures for nonlinear integral equations
Donald G. Anderson · 1965
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Computer Methods for Ordinary Differential Equations and DifferentialAlgebraic Equations
Uri M. Ascher and Linda R. Petzold · 1998
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Introduction to Numerical Analysis , volume 12
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Adam: A Method for Stochastic Optimization
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The cramer distance as a solution to biased wasserstein gradients
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GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
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Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics
Alex Kendall, Yarin Gal, and Roberto Cipolla · 2018
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Interpolating between Optimal Transport and MMD using Sinkhorn Divergences
Jean Feydy, Thibault Séjourné, François-Xavier Vialard, Shun-ichi Amari, Alain Trouvé, and Gabriel Peyré · 2019
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A Style-Based Generator Architecture for Generative Adversarial Networks
Tero Karras, Samuli Laine, and Timo Aila · 2019
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StarGAN v2: Diverse Image Synthesis for Multiple Domains
Yunjey Choi, Youngjung Uh, Jaejun Yoo, and Jung-Woo Ha · 2020
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Denoising Diffusion Probabilistic Models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Diffusion Schrödinger Bridge with Applications to Score-Based Generative Modeling
Valentin De Bortoli, James Thornton, Jeremy Heng, and Arnaud Doucet · 2021
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Diffusion Models Beat GANs on Image Synthesis
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Minimizing Trajectory Curvature of ODE-based Generative Models
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Your Diffusion Model is Secretly a Zero-Shot Classifier
Alexander C. Li, Mihir Prabhudesai, Shivam Duggal, Ellis Brown, and Deepak Pathak · 2023
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Flow Matching for Generative Modeling
Yaron Lipman, Ricky T. Q. Chen, Heli Ben-Hamu, Maximilian Nickel, and Matthew Le · 2023
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Diffusion bridge mixture transports, schrödinger bridge problems and generative modeling
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Multisample Flow Matching: Straightening Flows with Minibatch Couplings
Aram-Alexandre Pooladian, Heli Ben-Hamu, Carles Domingo-Enrich, Brandon Amos, Yaron Lipman, and Ricky T. Q. Chen · 2023
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Diffusion Schrödinger Bridge Matching
Yuyang Shi, Valentin De Bortoli, Andrew Campbell, and Arnaud Doucet · 2023
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Tim Dockhorn, Arash Vahdat, and Karsten Kreis · 2022
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Classifier-Free Diffusion Guidance
Jonathan Ho and Tim Salimans · 2022
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Rectified Flow: A Marginal Preserving Approach to Optimal Transport
Qiang Liu · 2022
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Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow
Xingchao Liu, Chengyue Gong, and Qiang Liu · 2022
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DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps
Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, and Jun Zhu · 2022
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SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations
Chenlin Meng, Yutong He, Yang Song, Jiaming Song, Jiajun Wu, Jun-Yan Zhu, and Stefano Ermon · 2022
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Consistency Models
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Improving and generalizing flow-based generative models with minibatch optimal transport
Alexander Tong, Kilian Fatras, Nikolay Malkin, Guillaume Huguet, Yanlei Zhang, Jarrid Rector-Brooks, Guy Wolf, and Yoshua Bengio · 2023
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Diffusion Probabilistic Model Made Slim
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Fast Sampling of Diffusion Models with Exponential Integrator
Qinsheng Zhang and Yongxin Chen · 2023
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HiPA: Enabling One-Step Text-to-Image Diffusion Models via High-Frequency-Promoting Adaptation
Yifan Zhang and Bryan Hooi · 2023
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Zhengyang Geng, Ashwini Pokle, William Luo, Justin Lin, and J. Zico Kolter · 2024
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Generalization in diffusion models arises from geometry-adaptive harmonic representations
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InstaFlow: One Step is Enough for High-Quality Diffusion-Based Text-to-Image Generation
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Fast ODE-based Sampling for Diffusion Models in Around 5 Steps
Zhenyu Zhou, Defang Chen, Can Wang, and Chun Chen · 2024
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