most citedSynthetic Data from Diffusion Models Improves ImageNet Classification

79 citations · 168 across the 7 of their papers we have counts for

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cs.CV2024

SpotlessSplats: Ignoring Distractors in 3D Gaussian Splatting

Sara Sabour, Lily Goli, George Kopanas +6

3D Gaussian Splatting (3DGS) is a promising technique for 3D reconstruction, offering efficient training and rendering speeds, making it suitable for real-time applications.However…

cs.CV2024

A Personalized Video-Based Hand Taxonomy: Application for Individuals with Spinal Cord Injury

Mehdy Dousty, David J. Fleet, José Zariffa

Hand function is critical for our interactions and quality of life. Spinal cord injuries (SCI) can impair hand function, reducing independence. A comprehensive evaluation of functi…

cs.CV202379 cited

Synthetic Data from Diffusion Models Improves ImageNet Classification

Shekoofeh Azizi, Simon Kornblith, Chitwan Saharia +2

Deep generative models are becoming increasingly powerful, now generating diverse high fidelity photo-realistic samples given text prompts. Have they reached the point where models…

cs.CV202325 cited

Monocular Depth Estimation using Diffusion Models

Saurabh Saxena, Abhishek Kar, Mohammad Norouzi +1

We formulate monocular depth estimation using denoising diffusion models, inspired by their recent successes in high fidelity image generation. To that end, we introduce innovation…

cs.CV202323 cited

Denoising Diffusion Probabilistic Models for Robust Image Super-Resolution in the Wild

Hshmat Sahak, Daniel Watson, Chitwan Saharia +1

Diffusion models have shown promising results on single-image super-resolution and other image- to-image translation tasks. Despite this success, they have not outperformed state-o…