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20212024
most citedAmbient Diffusion: Learning Clean Distributions from Corrupted Data

10 citations · 23 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG2024

How much is a noisy image worth? Data Scaling Laws for Ambient Diffusion

Giannis Daras, Yeshwanth Cherapanamjeri, Constantinos Daskalakis

The quality of generative models depends on the quality of the data they are trained on. Creating large-scale, high-quality datasets is often expensive and sometimes impossible, e.…

cs.LG202310 cited

Ambient Diffusion: Learning Clean Distributions from Corrupted Data

Giannis Daras, Kulin Shah, Yuval Dagan +3

We present the first diffusion-based framework that can learn an unknown distribution using only highly-corrupted samples. This problem arises in scientific applications where acce…

cs.LG20233 cited

Restoration-Degradation Beyond Linear Diffusions: A Non-Asymptotic Analysis For DDIM-Type Samplers

Sitan Chen, Giannis Daras, Alexandros G. Dimakis

We develop a framework for non-asymptotic analysis of deterministic samplers used for diffusion generative modeling. Several recent works have analyzed stochastic samplers using to…

cs.LG20237 cited

Consistent Diffusion Models: Mitigating Sampling Drift by Learning to be Consistent

Giannis Daras, Yuval Dagan, Alexandros G. Dimakis +1

Imperfect score-matching leads to a shift between the training and the sampling distribution of diffusion models. Due to the recursive nature of the generation process, errors in p…

cs.CV20213 cited

Solving Inverse Problems with NerfGANs

Giannis Daras, Wen-Sheng Chu, Abhishek Kumar +2

We introduce a novel framework for solving inverse problems using NeRF-style generative models. We are interested in the problem of 3-D scene reconstruction given a single 2-D imag…