{"list":"other","total":54,"rows":[{"id":"2006.11239","title":"Denoising Diffusion Probabilistic Models","created":"2020-06-19","cited_by_count":21076},{"id":"2112.10752","title":"High-Resolution Image Synthesis with Latent Diffusion Models","created":"2021-12-20","cited_by_count":18863},{"id":"1801.03924","title":"The Unreasonable Effectiveness of Deep Features as a Perceptual Metric","created":"2018-01-11","cited_by_count":11177},{"id":"2011.13456","title":"Score-Based Generative Modeling through Stochastic Differential Equations","created":"2020-11-26","cited_by_count":9122},{"id":"2105.05233","title":"Diffusion Models Beat GANs on Image Synthesis","created":"2021-05-11","cited_by_count":8255},{"id":"1503.03585","title":"Deep Unsupervised Learning using Nonequilibrium Thermodynamics","created":"2015-03-12","cited_by_count":7058},{"id":"1411.7766","title":"Deep Learning Face Attributes in the Wild","created":"2014-11-28","cited_by_count":3693},{"id":"1710.10196","title":"Progressive Growing of GANs for Improved Quality, Stability, and Variation","created":"2017-10-27","cited_by_count":2988},{"id":"2108.01073","title":"SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations","created":"2021-08-02","cited_by_count":1937},{"id":"2104.07636","title":"Image Super-Resolution via Iterative Refinement","created":"2021-04-15","cited_by_count":1511},{"id":"2209.14687","title":"Diffusion Posterior Sampling for General Noisy Inverse Problems","created":"2022-09-29","cited_by_count":1416},{"id":"2009.09761","title":"DiffWave: A Versatile Diffusion Model for Audio Synthesis","created":"2020-09-21","cited_by_count":1354},{"id":"2111.05826","title":"Palette: Image-to-Image Diffusion Models","created":"2021-11-10","cited_by_count":1300},{"id":"2201.11793","title":"Denoising Diffusion Restoration Models","created":"2022-01-27","cited_by_count":963},{"id":"2106.05931","title":"Score-based Generative Modeling in Latent Space","created":"2021-06-10","cited_by_count":636},{"id":"2108.02938","title":"ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models","created":"2021-08-06","cited_by_count":635},{"id":"2212.00490","title":"Zero-Shot Image Restoration Using Denoising Diffusion Null-Space Model","created":"2022-12-01","cited_by_count":583},{"id":"2111.08005","title":"Solving Inverse Problems in Medical Imaging with Score-Based Generative Models","created":"2021-11-15","cited_by_count":521},{"id":"2206.00941","title":"Improving Diffusion Models for Inverse Problems using Manifold Constraints","created":"2022-06-02","cited_by_count":508},{"id":"2402.17177","title":"Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models","created":"2024-02-27","cited_by_count":477},{"id":"1811.08839","title":"fastMRI: An Open Dataset and Benchmarks for Accelerated MRI","created":"2018-11-21","cited_by_count":470},{"id":"1704.00447","title":"Learning a Variational Network for Reconstruction of Accelerated MRI Data","created":"2017-04-03","cited_by_count":342},{"id":"1703.03208","title":"Compressed Sensing using Generative Models","created":"2017-03-09","cited_by_count":339},{"id":"2303.08320","title":"VideoFusion: Decomposed Diffusion Models for High-Quality Video Generation","created":"2023-03-15","cited_by_count":331},{"id":"2110.05243","title":"Score-based diffusion models for accelerated MRI","created":"2021-10-08","cited_by_count":311},{"id":"2108.01368","title":"Robust Compressed Sensing MRI with Deep Generative Priors","created":"2021-08-03","cited_by_count":286},{"id":"1704.02422","title":"A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction","created":"2017-04-08","cited_by_count":232},{"id":"1704.08841","title":"Image reconstruction by domain transform manifold learning","created":"2017-04-28","cited_by_count":232},{"id":"2206.09012","title":"Diffusion models as plug-and-play priors","created":"2022-06-17","cited_by_count":231},{"id":"2305.04391","title":"A Variational Perspective on Solving Inverse Problems with Diffusion Models","created":"2023-05-07","cited_by_count":222},{"id":"2105.14951","title":"SNIPS: Solving Noisy Inverse Problems Stochastically","created":"2021-05-31","cited_by_count":191},{"id":"2104.01409","title":"Diff-TTS: A Denoising Diffusion Model for Text-to-Speech","created":"2021-04-03","cited_by_count":157},{"id":"2211.10656","title":"Parallel Diffusion Models of Operator and Image for Blind Inverse Problems","created":"2022-11-19","cited_by_count":113},{"id":"2402.10009","title":"Zero-Shot Unsupervised and Text-Based Audio Editing Using DDPM Inversion","created":"2024-02-15","cited_by_count":60},{"id":"2301.12686","title":"GibbsDDRM: A Partially Collapsed Gibbs Sampler for Solving Blind Inverse Problems with Denoising Diffusion Restoration","created":"2023-01-30","cited_by_count":59},{"id":"1902.03051","title":"Reducing Uncertainty in Undersampled MRI Reconstruction with Active Acquisition","created":"2019-02-08","cited_by_count":56},{"id":"1806.06438","title":"Compressed Sensing with Deep Image Prior and Learned Regularization","created":"2018-06-17","cited_by_count":47},{"id":"1905.06723","title":"Deep Compressed Sensing","created":"2019-05-16","cited_by_count":46},{"id":"2007.10469","title":"Active MR k-space Sampling with Reinforcement Learning","created":"2020-07-20","cited_by_count":46},{"id":"2010.16262","title":"Experimental design for MRI by greedy policy search","created":"2020-10-30","cited_by_count":40},{"id":"2305.13128","title":"GSURE-Based Diffusion Model Training with Corrupted Data","created":"2023-05-22","cited_by_count":40},{"id":"2301.08227","title":"Diffusion-based Conditional ECG Generation with Structured State Space Models","created":"2023-01-19","cited_by_count":36},{"id":"2309.00287","title":"Fast Diffusion EM: a diffusion model for blind inverse problems with application to deconvolution","created":"2023-09-01","cited_by_count":36},{"id":"2006.09461","title":"Robust Compressed Sensing using Generative Models","created":"2020-06-16","cited_by_count":33},{"id":"2211.13926","title":"Generative Modeling in Sinogram Domain for Sparse-view CT Reconstruction","created":"2022-11-25","cited_by_count":27},{"id":"1901.04547","title":"Self-Supervised Deep Active Accelerated MRI","created":"2019-01-14","cited_by_count":24},{"id":"1906.11471","title":"Deep Active Learning with Adaptive Acquisition","created":"2019-06-27","cited_by_count":17},{"id":"2006.02420","title":"Learning to Scan: A Deep Reinforcement Learning Approach for Personalized Scanning in CT Imaging","created":"2020-06-03","cited_by_count":13},{"id":"1306.6239","title":"Near-Optimal Adaptive Compressed Sensing","created":"2013-06-26","cited_by_count":8},{"id":"1912.02938","title":"Lower Bounds for Compressed Sensing with Generative Models","created":"2019-12-06","cited_by_count":8},{"id":"2307.06343","title":"Sequential Experimental Design for X-Ray CT Using Deep Reinforcement Learning","created":"2023-07-12","cited_by_count":5},{"id":"2211.01670","title":"Active CT Reconstruction with a Learned Sampling Policy","created":"2022-11-03","cited_by_count":3},{"id":"2101.02746","title":"Learning Guided Electron Microscopy with Active Acquisition","created":"2021-01-07","cited_by_count":2},{"id":"1804.09673","title":"Improved Algorithms for Adaptive Compressed Sensing","created":"2018-04-25","cited_by_count":1}],"next_cursor":null}