{"list":"other","total":83,"rows":[{"id":"1512.03385","title":"Deep Residual Learning for Image Recognition","created":"2015-12-10","cited_by_count":52096},{"id":"1412.6980","title":"Adam: A Method for Stochastic Optimization","created":"2014-12-22","cited_by_count":37240},{"id":"2010.11929","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","created":"2020-10-22","cited_by_count":30186},{"id":"2006.11239","title":"Denoising Diffusion Probabilistic Models","created":"2020-06-19","cited_by_count":21076},{"id":"1505.04597","title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","created":"2015-05-18","cited_by_count":19713},{"id":"2105.05233","title":"Diffusion Models Beat GANs on Image Synthesis","created":"2021-05-11","cited_by_count":8255},{"id":"1710.10196","title":"Progressive Growing of GANs for Improved Quality, Stability, and Variation","created":"2017-10-27","cited_by_count":2988},{"id":"2006.09661","title":"Implicit Neural Representations with Periodic Activation Functions","created":"2020-06-17","cited_by_count":2238},{"id":"2102.12122","title":"Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions","created":"2021-02-24","cited_by_count":1962},{"id":"2108.10257","title":"SwinIR: Image Restoration Using Swin Transformer","created":"2021-08-23","cited_by_count":1887},{"id":"2111.09881","title":"Restormer: Efficient Transformer for High-Resolution Image Restoration","created":"2021-11-18","cited_by_count":1627},{"id":"1807.03039","title":"Glow: Generative Flow with Invertible 1x1 Convolutions","created":"2018-07-09","cited_by_count":1545},{"id":"1703.06211","title":"Deformable Convolutional Networks","created":"2017-03-17","cited_by_count":1488},{"id":"2105.01601","title":"MLP-Mixer: An all-MLP Architecture for Vision","created":"2021-05-04","cited_by_count":1446},{"id":"2209.14687","title":"Diffusion Posterior Sampling for General Noisy Inverse Problems","created":"2022-09-29","cited_by_count":1416},{"id":"1608.03981","title":"Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising","created":"2016-08-13","cited_by_count":1358},{"id":"1506.03365","title":"LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop","created":"2015-06-10","cited_by_count":1355},{"id":"2104.13478","title":"Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges","created":"2021-04-27","cited_by_count":1167},{"id":"1602.07576","title":"Group Equivariant Convolutional Networks","created":"2016-02-24","cited_by_count":1165},{"id":"1812.02822","title":"Learning Implicit Fields for Generative Shape Modeling","created":"2018-12-06","cited_by_count":1060},{"id":"2201.11793","title":"Denoising Diffusion Restoration Models","created":"2022-01-27","cited_by_count":963},{"id":"2012.00364","title":"Pre-Trained Image Processing Transformer","created":"2020-12-01","cited_by_count":893},{"id":"2106.03106","title":"Uformer: A General U-Shaped Transformer for Image Restoration","created":"2021-06-06","cited_by_count":866},{"id":"2003.04618","title":"Convolutional Occupancy Networks","created":"2020-03-10","cited_by_count":761},{"id":"2012.09161","title":"Learning Continuous Image Representation with Local Implicit Image Function","created":"2020-12-16","cited_by_count":552},{"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":"1806.03962","title":"Rotation Equivariant CNNs for Digital Pathology","created":"2018-06-08","cited_by_count":422},{"id":"1704.03264","title":"Learning Deep CNN Denoiser Prior for Image Restoration","created":"2017-04-11","cited_by_count":408},{"id":"1911.10414","title":"SAL: Sign Agnostic Learning of Shapes from Raw Data","created":"2019-11-23","cited_by_count":390},{"id":"1712.02862","title":"MoDL: Model Based Deep Learning Architecture for Inverse Problems","created":"2017-12-07","cited_by_count":387},{"id":"2003.08981","title":"Local Implicit Grid Representations for 3D Scenes","created":"2020-03-19","cited_by_count":373},{"id":"2103.16302","title":"Rethinking Spatial Dimensions of Vision Transformers","created":"2021-03-30","cited_by_count":361},{"id":"2305.08995","title":"Denoising Diffusion Models for Plug-and-Play Image Restoration","created":"2023-05-15","cited_by_count":340},{"id":"1703.03208","title":"Compressed Sensing using Generative Models","created":"2017-03-09","cited_by_count":339},{"id":"2003.10983","title":"Deep Local Shapes: Learning Local SDF Priors for Detailed 3D Reconstruction","created":"2020-03-24","cited_by_count":321},{"id":"1611.02862","title":"The Little Engine that Could: Regularization by Denoising (RED)","created":"2016-11-09","cited_by_count":307},{"id":"1612.08498","title":"Steerable CNNs","created":"2016-12-27","cited_by_count":305},{"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":"1703.02719","title":"Large Kernel Matters -- Improve Semantic Segmentation by Global Convolutional Network","created":"2017-03-08","cited_by_count":208},{"id":"2201.09792","title":"Patches Are All You Need?","created":"2022-01-24","cited_by_count":201},{"id":"2201.12204","title":"From data to functa: Your data point is a function and you can treat it like one","created":"2022-01-28","cited_by_count":187},{"id":"1805.07071","title":"Multi-level Wavelet-CNN for Image Restoration","created":"2018-05-18","cited_by_count":184},{"id":"2110.03336","title":"Frame Averaging for Invariant and Equivariant Network Design","created":"2021-10-07","cited_by_count":168},{"id":"2304.12526","title":"Patch Diffusion: Faster and More Data-Efficient Training of Diffusion Models","created":"2023-04-25","cited_by_count":133},{"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":"1709.02576","title":"Deep learning for undersampled MRI reconstruction","created":"2017-09-08","cited_by_count":91},{"id":"1906.05478","title":"Robust and interpretable blind image denoising via bias-free convolutional neural networks","created":"2019-06-13","cited_by_count":90},{"id":"2102.04776","title":"Generative Models as Distributions of Functions","created":"2021-02-09","cited_by_count":89},{"id":"1710.04011","title":"A Review of Convolutional Neural Networks for Inverse Problems in Imaging","created":"2017-10-11","cited_by_count":81},{"id":"1801.06756","title":"Denoising Prior Driven Deep Neural Network for Image Restoration","created":"2018-01-21","cited_by_count":80},{"id":"1903.08616","title":"Plug and play methods for magnetic resonance imaging (long version)","created":"2019-03-20","cited_by_count":75},{"id":"2011.14214","title":"Truly shift-invariant convolutional neural networks","created":"2020-11-28","cited_by_count":53},{"id":"2308.01316","title":"Patched Denoising Diffusion Models For High-Resolution Image Synthesis","created":"2023-08-02","cited_by_count":45},{"id":"2002.09611","title":"Tuning-free Plug-and-Play Proximal Algorithm for Inverse Imaging Problems","created":"2020-02-22","cited_by_count":38},{"id":"2102.10461","title":"Trumpets: Injective Flows for Inference and Inverse Problems","created":"2021-02-20","cited_by_count":36},{"id":"2203.08213","title":"HUMUS-Net: Hybrid unrolled multi-scale network architecture for accelerated MRI reconstruction","created":"2022-03-15","cited_by_count":30},{"id":"2404.16451","title":"Latent Modulated Function for Computational Optimal Continuous Image Representation","created":"2024-04-25","cited_by_count":21},{"id":"2012.05703","title":"TFPnP: Tuning-free Plug-and-Play Proximal Algorithm with Applications to Inverse Imaging Problems","created":"2020-11-18","cited_by_count":20},{"id":"1309.7643","title":"Rotationally Invariant Image Representation for Viewing Direction Classification in Cryo-EM","created":"2013-09-29","cited_by_count":17},{"id":"2303.07271","title":"Provably Convergent Plug-and-Play Quasi-Newton Methods","created":"2023-03-09","cited_by_count":15},{"id":"2109.12880","title":"Wasserstein Patch Prior for Image Superresolution","created":"2021-09-27","cited_by_count":13},{"id":"2204.07664","title":"Conditional Injective Flows for Bayesian Imaging","created":"2022-04-15","cited_by_count":13},{"id":"2307.09441","title":"Convergent regularization in inverse problems and linear plug-and-play denoisers","created":"2023-07-18","cited_by_count":13},{"id":"2310.01391","title":"A Restoration Network as an Implicit Prior","created":"2023-10-02","cited_by_count":13},{"id":"2011.12815","title":"Learning Multiscale Convolutional Dictionaries for Image Reconstruction","created":"2020-11-25","cited_by_count":11},{"id":"2105.04040","title":"Truly shift-equivariant convolutional neural networks with adaptive polyphase upsampling","created":"2021-05-09","cited_by_count":10},{"id":"2202.02018","title":"Image-to-Image MLP-mixer for Image Reconstruction","created":"2022-02-04","cited_by_count":9},{"id":"2206.02027","title":"Implicit Neural Representation for Mesh-Free Inverse Obstacle Scattering","created":"2022-06-04","cited_by_count":9},{"id":"2312.16611","title":"Learning from small data sets: Patch-based regularizers in inverse problems for image reconstruction","created":"2023-12-27","cited_by_count":9},{"id":"2305.12672","title":"Block Coordinate Plug-and-Play Methods for Blind Inverse Problems","created":"2023-05-22","cited_by_count":8},{"id":"2006.05854","title":"Learning the geometry of wave-based imaging","created":"2020-06-10","cited_by_count":7},{"id":"2403.17902","title":"Serpent: Scalable and Efficient Image Restoration via Multi-scale Structured State Space Models","created":"2024-03-26","cited_by_count":7},{"id":"2210.13983","title":"Optimization for Amortized Inverse Problems","created":"2022-10-25","cited_by_count":6},{"id":"2312.00125","title":"Scalable Bayesian uncertainty quantification with data-driven priors for radio interferometric imaging","created":"2023-11-30","cited_by_count":6},{"id":"2110.08092","title":"Equivariant and Invariant Reynolds Networks","created":"2021-10-15","cited_by_count":4},{"id":"2212.14042","title":"FunkNN: Neural Interpolation for Functional Generation","created":"2022-12-20","cited_by_count":4},{"id":"2301.03092","title":"Deep Injective Prior for Inverse Scattering","created":"2023-01-08","cited_by_count":3},{"id":"2306.12321","title":"Dynamic Implicit Image Function for Efficient Arbitrary-Scale Image Representation","created":"2023-06-21","cited_by_count":3},{"id":"2406.12082","title":"Uncertainty modeling for fine-tuned implicit functions","created":"2024-06-17","cited_by_count":2},{"id":"2405.08958","title":"Learned radio interferometric imaging for varying visibility coverage","created":"2024-05-14","cited_by_count":1},{"id":"2408.08115","title":"Learned denoising with simulated and experimental low-dose CT data","created":"2024-08-15","cited_by_count":1}],"next_cursor":null}