{"list":"other","total":118,"rows":[{"id":"1706.03762","title":"Attention Is All You Need","created":"2017-06-12","cited_by_count":60361},{"id":"1512.03385","title":"Deep Residual Learning for Image Recognition","created":"2015-12-10","cited_by_count":52096},{"id":"1810.04805","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","created":"2018-10-11","cited_by_count":40399},{"id":"2006.11239","title":"Denoising Diffusion Probabilistic Models","created":"2020-06-19","cited_by_count":21076},{"id":"1409.1556","title":"Very Deep Convolutional Networks for Large-Scale Image Recognition","created":"2014-09-04","cited_by_count":15364},{"id":"2103.14030","title":"Swin Transformer: Hierarchical Vision Transformer using Shifted Windows","created":"2021-03-25","cited_by_count":12139},{"id":"1801.03924","title":"The Unreasonable Effectiveness of Deep Features as a Perceptual Metric","created":"2018-01-11","cited_by_count":11177},{"id":"1503.02531","title":"Distilling the Knowledge in a Neural Network","created":"2015-03-09","cited_by_count":10324},{"id":"1608.06993","title":"Densely Connected Convolutional Networks","created":"2016-08-25","cited_by_count":6720},{"id":"1812.04948","title":"A Style-Based Generator Architecture for Generative Adversarial Networks","created":"2018-12-12","cited_by_count":6013},{"id":"1512.00567","title":"Rethinking the Inception Architecture for Computer Vision","created":"2015-12-02","cited_by_count":5475},{"id":"1709.01507","title":"Squeeze-and-Excitation Networks","created":"2017-09-05","cited_by_count":4789},{"id":"1411.7766","title":"Deep Learning Face Attributes in the Wild","created":"2014-11-28","cited_by_count":3693},{"id":"1603.08155","title":"Perceptual Losses for Real-Time Style Transfer and Super-Resolution","created":"2016-03-27","cited_by_count":2694},{"id":"1610.02357","title":"Xception: Deep Learning with Depthwise Separable Convolutions","created":"2016-10-07","cited_by_count":2332},{"id":"1711.07971","title":"Non-local Neural Networks","created":"2017-11-21","cited_by_count":2293},{"id":"1511.06434","title":"Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks","created":"2015-11-19","cited_by_count":2054},{"id":"1609.04802","title":"Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network","created":"2016-09-15","cited_by_count":1908},{"id":"2108.10257","title":"SwinIR: Image Restoration Using Swin Transformer","created":"2021-08-23","cited_by_count":1887},{"id":"1412.6550","title":"FitNets: Hints for Thin Deep Nets","created":"2014-12-19","cited_by_count":1653},{"id":"1707.02921","title":"Enhanced Deep Residual Networks for Single Image Super-Resolution","created":"2017-07-10","cited_by_count":1552},{"id":"1602.07261","title":"Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning","created":"2016-02-23","cited_by_count":1512},{"id":"2104.07636","title":"Image Super-Resolution via Iterative Refinement","created":"2021-04-15","cited_by_count":1511},{"id":"1807.02758","title":"Image Super-Resolution Using Very Deep Residual Channel Attention Networks","created":"2018-07-08","cited_by_count":1264},{"id":"1809.00219","title":"ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks","created":"2018-09-01","cited_by_count":1254},{"id":"1609.05158","title":"Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network","created":"2016-09-16","cited_by_count":1204},{"id":"1511.04587","title":"Accurate Image Super-Resolution Using Very Deep Convolutional Networks","created":"2015-11-14","cited_by_count":903},{"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":"2004.07728","title":"Image Quality Assessment: Unifying Structure and Texture Similarity","created":"2020-04-16","cited_by_count":811},{"id":"1802.08797","title":"Residual Dense Network for Image Super-Resolution","created":"2018-02-24","cited_by_count":799},{"id":"1907.11922","title":"MaskGAN: Towards Diverse and Interactive Facial Image Manipulation","created":"2019-07-27","cited_by_count":628},{"id":"1711.06077","title":"The Perception-Distortion Tradeoff","created":"2017-11-16","cited_by_count":587},{"id":"1508.06576","title":"A Neural Algorithm of Artistic Style","created":"2015-08-26","cited_by_count":483},{"id":"1704.03915","title":"Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution","created":"2017-04-12","cited_by_count":453},{"id":"2104.14951","title":"SRDiff: Single Image Super-Resolution with Diffusion Probabilistic Models","created":"2021-04-30","cited_by_count":415},{"id":"1904.00523","title":"Toward Real-World Single Image Super-Resolution: A New Benchmark and A New Model","created":"2019-04-01","cited_by_count":403},{"id":"1608.00367","title":"Accelerating the Super-Resolution Convolutional Neural Network","created":"2016-08-01","cited_by_count":394},{"id":"1804.02815","title":"Recovering Realistic Texture in Image Super-resolution by Deep Spatial Feature Transform","created":"2018-04-09","cited_by_count":371},{"id":"1902.06068","title":"Deep Learning for Image Super-resolution: A Survey","created":"2019-02-16","cited_by_count":369},{"id":"2101.04061","title":"Towards Real-World Blind Face Restoration with Generative Facial Prior","created":"2021-01-11","cited_by_count":349},{"id":"1905.01164","title":"SinGAN: Learning a Generative Model from a Single Natural Image","created":"2019-05-02","cited_by_count":348},{"id":"2006.04139","title":"Learning Texture Transformer Network for Image Super-Resolution","created":"2020-06-07","cited_by_count":347},{"id":"1904.05835","title":"Variational Information Distillation for Knowledge Transfer","created":"2019-04-11","cited_by_count":324},{"id":"1511.04491","title":"Deeply-Recursive Convolutional Network for Image Super-Resolution","created":"2015-11-14","cited_by_count":317},{"id":"1712.06087","title":"\"Zero-Shot\" Super-Resolution using Deep Internal Learning","created":"2017-12-17","cited_by_count":286},{"id":"2008.08767","title":"Single Image Super-Resolution via a Holistic Attention Network","created":"2020-08-20","cited_by_count":265},{"id":"1803.08664","title":"Fast, Accurate, and Lightweight Super-Resolution with Cascading Residual Network","created":"2018-03-23","cited_by_count":264},{"id":"2008.01928","title":"Component Divide-and-Conquer for Real-World Image Super-Resolution","created":"2020-08-05","cited_by_count":246},{"id":"1708.02209","title":"MemNet: A Persistent Memory Network for Image Restoration","created":"2017-08-07","cited_by_count":244},{"id":"1807.00734","title":"The relativistic discriminator: a key element missing from standard GAN","created":"2018-07-02","cited_by_count":238},{"id":"1903.10082","title":"Residual Non-local Attention Networks for Image Restoration","created":"2019-03-24","cited_by_count":236},{"id":"1712.06116","title":"Learning a Single Convolutional Super-Resolution Network for Multiple Degradations","created":"2017-12-17","cited_by_count":228},{"id":"1909.11856","title":"Lightweight Image Super-Resolution with Information Multi-distillation Network","created":"2019-09-26","cited_by_count":227},{"id":"1904.03377","title":"Blind Super-Resolution With Iterative Kernel Correction","created":"2019-04-06","cited_by_count":203},{"id":"1903.00875","title":"Meta-SR: A Magnification-Arbitrary Network for Super-Resolution","created":"2019-03-03","cited_by_count":181},{"id":"2007.12142","title":"PIPAL: a Large-Scale Image Quality Assessment Dataset for Perceptual Image Restoration","created":"2020-07-23","cited_by_count":178},{"id":"2104.00416","title":"Unsupervised Degradation Representation Learning for Blind Super-Resolution","created":"2021-04-01","cited_by_count":177},{"id":"1806.02919","title":"Non-Local Recurrent Network for Image Restoration","created":"2018-06-07","cited_by_count":160},{"id":"1809.07517","title":"The 2018 PIRM Challenge on Perceptual Image Super-resolution","created":"2018-09-20","cited_by_count":156},{"id":"1903.09814","title":"Feedback Network for Image Super-Resolution","created":"2019-03-23","cited_by_count":155},{"id":"2108.11084","title":"Transformer for Single Image Super-Resolution","created":"2021-08-25","cited_by_count":150},{"id":"1908.06382","title":"RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-Resolution","created":"2019-08-18","cited_by_count":145},{"id":"1612.05890","title":"Learning a No-Reference Quality Metric for Single-Image Super-Resolution","created":"2016-12-18","cited_by_count":144},{"id":"1809.00437","title":"Unsupervised Image Super-Resolution using Cycle-in-Cycle Generative Adversarial Networks","created":"2018-09-03","cited_by_count":133},{"id":"2010.02631","title":"Unfolding the Alternating Optimization for Blind Super Resolution","created":"2020-10-06","cited_by_count":129},{"id":"2009.11551","title":"Residual Feature Distillation Network for Lightweight Image Super-Resolution","created":"2020-09-24","cited_by_count":122},{"id":"1507.06550","title":"Human Pose Estimation with Iterative Error Feedback","created":"2015-07-23","cited_by_count":121},{"id":"1803.09454","title":"Fast and Accurate Single Image Super-Resolution via Information Distillation Network","created":"2018-03-26","cited_by_count":121},{"id":"1903.00834","title":"Image Super-Resolution by Neural Texture Transfer","created":"2019-03-03","cited_by_count":120},{"id":"2003.13081","title":"Structure-Preserving Super Resolution with Gradient Guidance","created":"2020-03-29","cited_by_count":116},{"id":"1807.11458","title":"To learn image super-resolution, use a GAN to learn how to do image degradation first","created":"2018-07-30","cited_by_count":94},{"id":"2107.03055","title":"Blind Image Super-Resolution: A Survey and Beyond","created":"2021-07-07","cited_by_count":93},{"id":"1807.10547","title":"CrossNet: An End-to-end Reference-based Super Resolution Network using Cross-scale Warping","created":"2018-07-27","cited_by_count":91},{"id":"2003.07018","title":"Closed-loop Matters: Dual Regression Networks for Single Image Super-Resolution","created":"2020-03-16","cited_by_count":90},{"id":"1904.07523","title":"A Deep Journey into Super-resolution: A survey","created":"2019-04-16","cited_by_count":86},{"id":"2004.01178","title":"Unsupervised Real-world Image Super Resolution via Domain-distance Aware Training","created":"2020-04-02","cited_by_count":83},{"id":"1412.1710","title":"Convolutional Neural Networks at Constrained Time Cost","created":"2014-12-04","cited_by_count":81},{"id":"2106.00783","title":"Fourier Space Losses for Efficient Perceptual Image Super-Resolution","created":"2021-06-01","cited_by_count":73},{"id":"2002.11397","title":"Unpaired Image Super-Resolution using Pseudo-Supervision","created":"2020-02-26","cited_by_count":67},{"id":"2004.03791","title":"Learning A Single Network for Scale-Arbitrary Super-Resolution","created":"2020-04-08","cited_by_count":66},{"id":"2103.02368","title":"Real-World Single Image Super-Resolution: A Brief Review","created":"2021-03-03","cited_by_count":65},{"id":"1803.01417","title":"Efficient and Accurate MRI Super-Resolution using a Generative Adversarial Network and 3D Multi-Level Densely Connected Network","created":"2018-03-04","cited_by_count":64},{"id":"2004.06965","title":"Unified Dynamic Convolutional Network for Super-Resolution with Variational Degradations","created":"2020-04-15","cited_by_count":61},{"id":"2106.06742","title":"Task Transformer Network for Joint MRI Reconstruction and Super-Resolution","created":"2021-06-12","cited_by_count":58},{"id":"1906.07078","title":"Exemplar Guided Face Image Super-Resolution without Facial Landmarks","created":"2019-06-17","cited_by_count":57},{"id":"1903.05784","title":"Learning Parallax Attention for Stereo Image Super-Resolution","created":"2019-03-14","cited_by_count":56},{"id":"1904.03378","title":"Camera Lens Super-Resolution","created":"2019-04-06","cited_by_count":56},{"id":"2007.07524","title":"Learning with Privileged Information for Efficient Image Super-Resolution","created":"2020-07-15","cited_by_count":53},{"id":"1511.02228","title":"Seven ways to improve example-based single image super resolution","created":"2015-11-06","cited_by_count":51},{"id":"2011.04212","title":"PAMS: Quantized Super-Resolution via Parameterized Max Scale","created":"2020-11-09","cited_by_count":48},{"id":"2005.08752","title":"Learning Spatial-Spectral Prior for Super-Resolution of Hyperspectral Imagery","created":"2020-05-18","cited_by_count":47},{"id":"1804.02900","title":"A Fully Progressive Approach to Single-Image Super-Resolution","created":"2018-04-09","cited_by_count":46},{"id":"1811.02328","title":"Super-Identity Convolutional Neural Network for Face Hallucination","created":"2018-11-06","cited_by_count":39},{"id":"2009.08250","title":"Parallax Attention for Unsupervised Stereo Correspondence Learning","created":"2020-09-16","cited_by_count":39},{"id":"1912.07849","title":"Spatial-Angular Interaction for Light Field Image Super-Resolution","created":"2019-12-17","cited_by_count":38},{"id":"1607.05046","title":"Deep Cascaded Bi-Network for Face Hallucination","created":"2016-07-18","cited_by_count":37},{"id":"2001.00704","title":"SAINT: Spatially Aware Interpolation NeTwork for Medical Slice Synthesis","created":"2020-01-03","cited_by_count":37},{"id":"2011.03802","title":"Symmetric Parallax Attention for Stereo Image Super-Resolution","created":"2020-11-07","cited_by_count":35},{"id":"2101.03749","title":"Deep Learning-based Face Super-Resolution: A Survey","created":"2021-01-11","cited_by_count":32}],"next_cursor":"100"}