{"list":"other","total":55,"rows":[{"id":"2006.11239","title":"Denoising Diffusion Probabilistic Models","created":"2020-06-19","cited_by_count":21076},{"id":"1801.03924","title":"The Unreasonable Effectiveness of Deep Features as a Perceptual Metric","created":"2018-01-11","cited_by_count":11177},{"id":"2010.02502","title":"Denoising Diffusion Implicit Models","created":"2020-10-06","cited_by_count":9781},{"id":"2011.13456","title":"Score-Based Generative Modeling through Stochastic Differential Equations","created":"2020-11-26","cited_by_count":9122},{"id":"1409.0575","title":"ImageNet Large Scale Visual Recognition Challenge","created":"2014-09-01","cited_by_count":8498},{"id":"1503.03585","title":"Deep Unsupervised Learning using Nonequilibrium Thermodynamics","created":"2015-03-12","cited_by_count":7058},{"id":"1907.05600","title":"Generative Modeling by Estimating Gradients of the Data Distribution","created":"2019-07-12","cited_by_count":4101},{"id":"1806.07366","title":"Neural Ordinary Differential Equations","created":"2018-06-19","cited_by_count":3779},{"id":"2206.00364","title":"Elucidating the Design Space of Diffusion-Based Generative Models","created":"2022-06-01","cited_by_count":3045},{"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":"2111.05826","title":"Palette: Image-to-Image Diffusion Models","created":"2021-11-10","cited_by_count":1300},{"id":"2107.00630","title":"Variational Diffusion Models","created":"2021-07-01","cited_by_count":1153},{"id":"1711.10925","title":"Deep Image Prior","created":"2017-11-29","cited_by_count":1098},{"id":"1912.01865","title":"StarGAN v2: Diverse Image Synthesis for Multiple Domains","created":"2019-12-04","cited_by_count":1098},{"id":"2209.15571","title":"Building Normalizing Flows with Stochastic Interpolants","created":"2022-09-30","cited_by_count":995},{"id":"2201.11793","title":"Denoising Diffusion Restoration Models","created":"2022-01-27","cited_by_count":963},{"id":"2101.09258","title":"Maximum Likelihood Training of Score-Based Diffusion Models","created":"2021-01-22","cited_by_count":672},{"id":"2108.02938","title":"ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models","created":"2021-08-06","cited_by_count":635},{"id":"1810.01367","title":"FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models","created":"2018-10-02","cited_by_count":594},{"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":"1611.03679","title":"Deep Convolutional Neural Network for Inverse Problems in Imaging","created":"2016-11-11","cited_by_count":433},{"id":"1704.03264","title":"Learning Deep CNN Denoiser Prior for Image Restoration","created":"2017-04-11","cited_by_count":408},{"id":"1703.03208","title":"Compressed Sensing using Generative Models","created":"2017-03-09","cited_by_count":339},{"id":"2305.08891","title":"Common Diffusion Noise Schedules and Sample Steps are Flawed","created":"2023-05-15","cited_by_count":310},{"id":"1611.02862","title":"The Little Engine that Could: Regularization by Denoising (RED)","created":"2016-11-09","cited_by_count":307},{"id":"2112.05146","title":"Come-Closer-Diffuse-Faster: Accelerating Conditional Diffusion Models for Inverse Problems through Stochastic Contraction","created":"2021-12-09","cited_by_count":307},{"id":"2108.01368","title":"Robust Compressed Sensing MRI with Deep Generative Priors","created":"2021-08-03","cited_by_count":286},{"id":"1605.01710","title":"Plug-and-Play ADMM for Image Restoration: Fixed Point Convergence and Applications","created":"2016-05-05","cited_by_count":246},{"id":"2206.09012","title":"Diffusion models as plug-and-play priors","created":"2022-06-17","cited_by_count":231},{"id":"2003.13659","title":"Exploiting Deep Generative Prior for Versatile Image Restoration and Manipulation","created":"2020-03-30","cited_by_count":225},{"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":"2307.00619","title":"Solving Linear Inverse Problems Provably via Posterior Sampling with Latent Diffusion Models","created":"2023-07-02","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":"1704.03488","title":"Learning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging Problems","created":"2017-04-11","cited_by_count":107},{"id":"2103.06191","title":"A Study of Face Obfuscation in ImageNet","created":"2021-03-10","cited_by_count":83},{"id":"2007.13640","title":"Solving Linear Inverse Problems Using the Prior Implicit in a Denoiser","created":"2020-07-27","cited_by_count":79},{"id":"2301.03362","title":"Image Denoising: The Deep Learning Revolution and Beyond -- A Survey Paper --","created":"2023-01-09","cited_by_count":71},{"id":"2102.07364","title":"Intermediate Layer Optimization for Inverse Problems using Deep Generative Models","created":"2021-02-15","cited_by_count":70},{"id":"1703.09912","title":"One Network to Solve Them All --- Solving Linear Inverse Problems using Deep Projection Models","created":"2017-03-29","cited_by_count":67},{"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":"1802.08406","title":"Solving Linear Inverse Problems Using GAN Priors: An Algorithm with Provable Guarantees","created":"2018-02-23","cited_by_count":58},{"id":"2008.00226","title":"Regularization by Denoising via Fixed-Point Projection (RED-PRO)","created":"2020-08-01","cited_by_count":55},{"id":"2002.11743","title":"Composing Normalizing Flows for Inverse Problems","created":"2020-02-26","cited_by_count":48},{"id":"1806.06438","title":"Compressed Sensing with Deep Image Prior and Learned Regularization","created":"2018-06-17","cited_by_count":47},{"id":"2306.06626","title":"On Kinetic Optimal Probability Paths for Generative Models","created":"2023-06-11","cited_by_count":42},{"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":"2003.08089","title":"Solving Inverse Problems with a Flow-based Noise Model","created":"2020-03-18","cited_by_count":31},{"id":"2305.16965","title":"Accelerating Diffusion Models for Inverse Problems through Shortcut Sampling","created":"2023-05-26","cited_by_count":21},{"id":"2212.04319","title":"On the Robustness of Normalizing Flows for Inverse Problems in Imaging","created":"2022-12-08","cited_by_count":8},{"id":"2305.12672","title":"Block Coordinate Plug-and-Play Methods for Blind Inverse Problems","created":"2023-05-22","cited_by_count":8},{"id":"2111.13329","title":"A Variational Inference Approach to Inverse Problems with Gamma Hyperpriors","created":"2021-11-26","cited_by_count":2}],"next_cursor":null}