{"list":"other","total":58,"rows":[{"id":"1512.03385","title":"Deep Residual Learning for Image Recognition","created":"2015-12-10","cited_by_count":52096},{"id":"2002.05709","title":"A Simple Framework for Contrastive Learning of Visual Representations","created":"2020-02-13","cited_by_count":12049},{"id":"2111.06377","title":"Masked Autoencoders Are Scalable Vision Learners","created":"2021-11-11","cited_by_count":7403},{"id":"2108.10257","title":"SwinIR: Image Restoration Using Swin Transformer","created":"2021-08-23","cited_by_count":1887},{"id":"1902.10811","title":"Do ImageNet Classifiers Generalize to ImageNet?","created":"2019-02-13","cited_by_count":1515},{"id":"1711.10925","title":"Deep Image Prior","created":"2017-11-29","cited_by_count":1098},{"id":"1509.01240","title":"Train faster, generalize better: Stability of stochastic gradient descent","created":"2015-09-03","cited_by_count":552},{"id":"1803.04189","title":"Noise2Noise: Learning Image Restoration without Clean Data","created":"2018-03-12","cited_by_count":533},{"id":"2008.13751","title":"Plug-and-Play Image Restoration with Deep Denoiser Prior","created":"2020-08-31","cited_by_count":470},{"id":"1611.03679","title":"Deep Convolutional Neural Network for Inverse Problems in Imaging","created":"2016-11-11","cited_by_count":433},{"id":"1811.10980","title":"Noise2Void - Learning Denoising from Single Noisy Images","created":"2018-11-27","cited_by_count":364},{"id":"1704.00447","title":"Learning a Variational Network for Reconstruction of Accelerated MRI Data","created":"2017-04-03","cited_by_count":342},{"id":"1612.08498","title":"Steerable CNNs","created":"2016-12-27","cited_by_count":305},{"id":"1901.11365","title":"Noise2Self: Blind Denoising by Self-Supervision","created":"2019-01-30","cited_by_count":276},{"id":"1707.06474","title":"Learned Primal-dual Reconstruction","created":"2017-07-20","cited_by_count":251},{"id":"2101.02824","title":"Neighbor2Neighbor: Self-Supervised Denoising from Single Noisy Images","created":"2021-01-08","cited_by_count":173},{"id":"1901.10277","title":"High-Quality Self-Supervised Deep Image Denoising","created":"2019-01-29","cited_by_count":161},{"id":"2110.09327","title":"Self-Supervised Representation Learning: Introduction, Advances and Challenges","created":"2021-10-18","cited_by_count":148},{"id":"1910.11908","title":"Noisier2Noise: Learning to Denoise from Unpaired Noisy Data","created":"2019-10-25","cited_by_count":126},{"id":"1810.03982","title":"Deep Decoder: Concise Image Representations from Untrained Non-convolutional Networks","created":"2018-10-02","cited_by_count":120},{"id":"1908.02197","title":"Neural Blind Deconvolution Using Deep Priors","created":"2019-08-06","cited_by_count":113},{"id":"2106.07009","title":"Noise2Score: Tweedie's Approach to Self-Supervised Image Denoising without Clean Images","created":"2021-06-13","cited_by_count":113},{"id":"2305.19256","title":"Ambient Diffusion: Learning Clean Distributions from Corrupted Data","created":"2023-05-30","cited_by_count":111},{"id":"2203.17061","title":"Plug-and-Play Methods for Integrating Physical and Learned Models in Computational Imaging","created":"2022-03-31","cited_by_count":96},{"id":"2203.06967","title":"Blind2Unblind: Self-Supervised Image Denoising with Visible Blind Spots","created":"2022-03-14","cited_by_count":84},{"id":"2103.14756","title":"Equivariant Imaging: Learning Beyond the Range Space","created":"2021-03-26","cited_by_count":83},{"id":"2405.13712","title":"Learning Diffusion Priors from Observations by Expectation Maximization","created":"2024-05-22","cited_by_count":61},{"id":"1904.07615","title":"Total Denoising: Unsupervised Learning of 3D Point Cloud Cleaning","created":"2019-04-16","cited_by_count":58},{"id":"2111.12855","title":"Robust Equivariant Imaging: a fully unsupervised framework for learning to image from noisy and partial measurements","created":"2021-11-25","cited_by_count":56},{"id":"1906.00651","title":"Probabilistic Noise2Void: Unsupervised Content-Aware Denoising","created":"2019-06-03","cited_by_count":51},{"id":"1811.12766","title":"Model-blind Video Denoising Via Frame-to-frame Training","created":"2018-11-30","cited_by_count":34},{"id":"2309.13598","title":"On the Posterior Distribution in Denoising: Application to Uncertainty Quantification","created":"2023-09-24","cited_by_count":33},{"id":"2403.08728","title":"Ambient Diffusion Posterior Sampling: Solving Inverse Problems with Diffusion Models Trained on Corrupted Data","created":"2024-03-13","cited_by_count":32},{"id":"2205.10278","title":"A theoretical framework for self-supervised MR image reconstruction using sub-sampling via variable density Noisier2Noise","created":"2022-05-20","cited_by_count":30},{"id":"2203.12513","title":"Sensing Theorems for Unsupervised Learning in Linear Inverse Problems","created":"2022-03-23","cited_by_count":28},{"id":"2505.20160","title":"DeepInverse: A Python package for solving imaging inverse problems with deep learning","created":"2025-05-26","cited_by_count":28},{"id":"2104.01374","title":"Interpretable Unsupervised Diversity Denoising and Artefact Removal","created":"2021-04-03","cited_by_count":26},{"id":"2201.12151","title":"Unsupervised Learning From Incomplete Measurements for Inverse Problems","created":"2022-01-28","cited_by_count":20},{"id":"2110.13148","title":"As if by magic: self-supervised training of deep despeckling networks with MERLIN","created":"2021-10-25","cited_by_count":18},{"id":"2112.03696","title":"Noise Distribution Adaptive Self-Supervised Image Denoising using Tweedie Distribution and Score Matching","created":"2021-12-05","cited_by_count":18},{"id":"1910.09116","title":"Self-Supervised Physics-Based Deep Learning MRI Reconstruction Without Fully-Sampled Data","created":"2019-10-21","cited_by_count":17},{"id":"1912.05098","title":"Memory-efficient Learning for Large-scale Computational Imaging -- NeurIPS deep inverse workshop","created":"2019-12-11","cited_by_count":17},{"id":"2104.03838","title":"Speech Denoising Without Clean Training Data: A Noise2Noise Approach","created":"2021-04-08","cited_by_count":17},{"id":"2310.11838","title":"Equivariant Bootstrapping for Uncertainty Quantification in Imaging Inverse Problems","created":"2023-10-18","cited_by_count":17},{"id":"2107.12815","title":"Adaptive Denoising via GainTuning","created":"2021-07-27","cited_by_count":16},{"id":"2409.01985","title":"UNSURE: self-supervised learning with Unknown Noise level and Stein's Unbiased Risk Estimate","created":"2024-09-03","cited_by_count":16},{"id":"2112.11079","title":"Data fission: splitting a single data point","created":"2021-12-21","cited_by_count":14},{"id":"2412.04648","title":"Generalized Recorrupted-to-Recorrupted: Self-Supervised Learning Beyond Gaussian Noise","created":"2024-12-05","cited_by_count":13},{"id":"1909.13482","title":"Extreme MRI: Large-Scale Volumetric Dynamic Imaging from Continuous Non-Gated Acquisitions","created":"2019-09-30","cited_by_count":12},{"id":"1910.10100","title":"The Practicality of Stochastic Optimization in Imaging Inverse Problems","created":"2019-10-22","cited_by_count":9},{"id":"2006.02379","title":"The Neural Tangent Link Between CNN Denoisers and Non-Local Filters","created":"2020-06-03","cited_by_count":9},{"id":"2210.13389","title":"A Regularized Conditional GAN for Posterior Sampling in Image Recovery Problems","created":"2022-10-24","cited_by_count":9},{"id":"1906.05775","title":"Training Image Estimators without Image Ground-Truth","created":"2019-06-13","cited_by_count":8},{"id":"2305.19079","title":"Analyzing the Sample Complexity of Self-Supervised Image Reconstruction Methods","created":"2023-05-30","cited_by_count":8},{"id":"1708.00745","title":"Efficient Inversion of Multiple-Scattering Model for Optical Diffraction Tomography","created":"2017-07-11","cited_by_count":6},{"id":"2303.08691","title":"Learning to Reconstruct Signals From Binary Measurements","created":"2023-03-15","cited_by_count":5},{"id":"2409.15283","title":"Equivariance-based self-supervised learning for audio signal recovery from clipped measurements","created":"2024-09-03","cited_by_count":5},{"id":"2205.11952","title":"3D helical CT Reconstruction with a Memory Efficient Learned Primal-Dual Architecture","created":"2022-05-24","cited_by_count":4}],"next_cursor":null}