Refining Deep Generative Models via Discriminator Gradient Flow

1 December 2020

Papers citing "Refining Deep Generative Models via Discriminator Gradient Flow"

11 / 11 papers shown

Title
Deep MMD Gradient Flow without adversarial training Alexandre Galashov Valentin De Bortoli Arthur Gretton DiffM 40 8 0 10 May 2024
Diffusion Models with Deterministic Normalizing Flow Priors Mohsen Zand Ali Etemad Michael A. Greenspan DiffM 34 2 0 03 Sep 2023
Refining 6-DoF Grasps with Context-Specific Classifiers Tasbolat Taunyazov Heng Zhang John Patrick Eala Na Zhao Harold Soh 29 2 0 14 Aug 2023
Generative Modeling through the Semi-dual Formulation of Unbalanced Optimal Transport Jaemoo Choi Jaewoong Choi Myung-joo Kang OT 39 19 0 24 May 2023
Block-wise Training of Residual Networks via the Minimizing Movement Scheme Skander Karkar Ibrahim Ayed Emmanuel de Bézenac Patrick Gallinari 33 1 0 03 Oct 2022
Maximum Likelihood Training of Implicit Nonlinear Diffusion Models Dongjun Kim Byeonghu Na S. Kwon Dongsoo Lee Wanmo Kang Il-Chul Moon DiffM 213 52 0 27 May 2022
Likelihood Training of Schrödinger Bridge using Forward-Backward SDEs Theory T. Chen Guan-Horng Liu Evangelos A. Theodorou DiffM OT 174 164 0 21 Oct 2021
Controllable and Compositional Generation with Latent-Space Energy-Based Models Weili Nie Arash Vahdat Anima Anandkumar 22 78 0 21 Oct 2021
Rethinking Multidimensional Discriminator Output for Generative Adversarial Networks M. Dai Haibin Hang A. Srivastava 18 3 0 08 Sep 2021
Score-based Generative Modeling in Latent Space Arash Vahdat Karsten Kreis Jan Kautz DiffM 16 659 0 10 Jun 2021
EBMs Trained with Maximum Likelihood are Generator Models Trained with a Self-adverserial Loss Zhisheng Xiao Qing Yan Y. Amit 32 2 0 23 Feb 2021