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Can Diffusion Model Achieve Better Performance in Text Generation?
  Bridging the Gap between Training and Inference!

Can Diffusion Model Achieve Better Performance in Text Generation? Bridging the Gap between Training and Inference!

8 May 2023
Zecheng Tang
Pinzheng Wang
Keyan Zhou
Juntao Li
Ziqiang Cao
M. Zhang
    DiffM
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Papers citing "Can Diffusion Model Achieve Better Performance in Text Generation? Bridging the Gap between Training and Inference!"

4 / 4 papers shown
Title
Denoising MCMC for Accelerating Diffusion-Based Generative Models
Denoising MCMC for Accelerating Diffusion-Based Generative Models
Beomsu Kim
Jong Chul Ye
DiffM
38
13
0
29 Sep 2022
Diffusion Models: A Comprehensive Survey of Methods and Applications
Diffusion Models: A Comprehensive Survey of Methods and Applications
Ling Yang
Zhilong Zhang
Yingxia Shao
Shenda Hong
Runsheng Xu
Yue Zhao
Wentao Zhang
Bin Cui
Ming-Hsuan Yang
DiffM
MedIm
224
1,296
0
02 Sep 2022
Diffusion-LM Improves Controllable Text Generation
Diffusion-LM Improves Controllable Text Generation
Xiang Lisa Li
John Thickstun
Ishaan Gulrajani
Percy Liang
Tatsunori B. Hashimoto
AI4CE
171
772
0
27 May 2022
Argmax Flows and Multinomial Diffusion: Learning Categorical
  Distributions
Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions
Emiel Hoogeboom
Didrik Nielsen
P. Jaini
Patrick Forré
Max Welling
DiffM
202
394
0
10 Feb 2021
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