Quantifying the Ease of Reproducing Training Data in Unconditional Diffusion Models

25 March 2025

Papers citing "Quantifying the Ease of Reproducing Training Data in Unconditional Diffusion Models"

6 / 6 papers shown

Title
Hierarchical Text-Conditional Image Generation with CLIP Latents Aditya A. Ramesh Prafulla Dhariwal Alex Nichol Casey Chu Mark Chen VLM DiffM 370 6,854 0 13 Apr 2022
Score-Based Generative Modeling through Stochastic Differential Equations Yang Song Jascha Narain Sohl-Dickstein Diederik P. Kingma Abhishek Kumar Stefano Ermon Ben Poole DiffM SyDa 325 6,444 0 26 Nov 2020
Denoising Diffusion Implicit Models Jiaming Song Chenlin Meng Stefano Ermon VLM DiffM 247 7,350 0 06 Oct 2020
Generative Modeling by Estimating Gradients of the Data Distribution Yang Song Stefano Ermon SyDa DiffM 228 3,893 0 12 Jul 2019
Deep Unsupervised Learning using Nonequilibrium Thermodynamics Jascha Narain Sohl-Dickstein Eric A. Weiss Niru Maheswaranathan Surya Ganguli SyDa DiffM 288 6,925 0 12 Mar 2015
What Regularized Auto-Encoders Learn from the Data Generating Distribution Guillaume Alain Yoshua Bengio OOD DRL 64 502 0 18 Nov 2012