{"list":"other","total":54,"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":"1801.03924","title":"The Unreasonable Effectiveness of Deep Features as a Perceptual Metric","created":"2018-01-11","cited_by_count":11177},{"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":"1411.7766","title":"Deep Learning Face Attributes in the Wild","created":"2014-11-28","cited_by_count":3693},{"id":"1912.04958","title":"Analyzing and Improving the Image Quality of StyleGAN","created":"2019-12-03","cited_by_count":3588},{"id":"1710.10196","title":"Progressive Growing of GANs for Improved Quality, Stability, and Variation","created":"2017-10-27","cited_by_count":2988},{"id":"1603.08155","title":"Perceptual Losses for Real-Time Style Transfer and Super-Resolution","created":"2016-03-27","cited_by_count":2694},{"id":"1609.04802","title":"Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network","created":"2016-09-15","cited_by_count":1908},{"id":"1707.02921","title":"Enhanced Deep Residual Networks for Single Image Super-Resolution","created":"2017-07-10","cited_by_count":1552},{"id":"1506.02025","title":"Spatial Transformer Networks","created":"2015-06-05","cited_by_count":1429},{"id":"1903.07291","title":"Semantic Image Synthesis with Spatially-Adaptive Normalization","created":"2019-03-18","cited_by_count":1299},{"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":"1501.00092","title":"Image Super-Resolution Using Deep Convolutional Networks","created":"2014-12-31","cited_by_count":1224},{"id":"1710.08092","title":"VGGFace2: A dataset for recognising faces across pose and age","created":"2017-10-23","cited_by_count":914},{"id":"1511.04587","title":"Accurate Image Super-Resolution Using Very Deep Convolutional Networks","created":"2015-11-14","cited_by_count":903},{"id":"1802.08797","title":"Residual Dense Network for Image Super-Resolution","created":"2018-02-24","cited_by_count":799},{"id":"1612.02177","title":"Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurring","created":"2016-12-07","cited_by_count":727},{"id":"1801.04406","title":"Which Training Methods for GANs do actually Converge?","created":"2018-01-13","cited_by_count":679},{"id":"1703.07332","title":"How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks)","created":"2017-03-21","cited_by_count":525},{"id":"1704.03915","title":"Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution","created":"2017-04-12","cited_by_count":453},{"id":"2007.06600","title":"Closed-Form Factorization of Latent Semantics in GANs","created":"2020-07-13","cited_by_count":408},{"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":"2003.03808","title":"PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models","created":"2020-03-08","cited_by_count":358},{"id":"1511.04491","title":"Deeply-Recursive Convolutional Network for Image Super-Resolution","created":"2015-11-14","cited_by_count":317},{"id":"1511.06523","title":"WIDER FACE: A Face Detection Benchmark","created":"2015-11-20","cited_by_count":298},{"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":"1904.03377","title":"Blind Super-Resolution With Iterative Kernel Correction","created":"2019-04-06","cited_by_count":203},{"id":"1711.10703","title":"FSRNet: End-to-End Learning Face Super-Resolution with Facial Priors","created":"2017-11-29","cited_by_count":170},{"id":"1808.03344","title":"Deep Learning for Single Image Super-Resolution: A Brief Review","created":"2018-08-09","cited_by_count":158},{"id":"1710.01992","title":"Fast and Accurate Image Super-Resolution with Deep Laplacian Pyramid Networks","created":"2017-10-04","cited_by_count":138},{"id":"1809.00437","title":"Unsupervised Image Super-Resolution using Cycle-in-Cycle Generative Adversarial Networks","created":"2018-09-03","cited_by_count":133},{"id":"1611.09577","title":"Fast Face-swap Using Convolutional Neural Networks","created":"2016-11-29","cited_by_count":119},{"id":"2003.13081","title":"Structure-Preserving Super Resolution with Gradient Guidance","created":"2020-03-29","cited_by_count":116},{"id":"1905.05169","title":"Zoom To Learn, Learn To Zoom","created":"2019-05-13","cited_by_count":115},{"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":"2004.01178","title":"Unsupervised Real-world Image Super Resolution via Domain-distance Aware Training","created":"2020-04-02","cited_by_count":83},{"id":"1908.08239","title":"Progressive Face Super-Resolution via Attention to Facial Landmark","created":"2019-08-22","cited_by_count":73},{"id":"2002.11397","title":"Unpaired Image Super-Resolution using Pseudo-Supervision","created":"2020-02-26","cited_by_count":67},{"id":"1909.09629","title":"Unsupervised Learning for Real-World Super-Resolution","created":"2019-09-20","cited_by_count":64},{"id":"1904.03378","title":"Camera Lens Super-Resolution","created":"2019-04-06","cited_by_count":56},{"id":"2012.01211","title":"Learning Spatial Attention for Face Super-Resolution","created":"2020-12-02","cited_by_count":55},{"id":"2003.13063","title":"Deep Face Super-Resolution with Iterative Collaboration between Attentive Recovery and Landmark Estimation","created":"2020-03-29","cited_by_count":53},{"id":"1804.02900","title":"A Fully Progressive Approach to Single-Image Super-Resolution","created":"2018-04-09","cited_by_count":46},{"id":"2003.12957","title":"Realistic Face Reenactment via Self-Supervised Disentangling of Identity and Pose","created":"2020-03-29","cited_by_count":26},{"id":"2102.09351","title":"A Comprehensive Review of Deep Learning-based Single Image Super-resolution","created":"2021-02-18","cited_by_count":23},{"id":"1805.02704","title":"Image Super-Resolution via Dual-State Recurrent Networks","created":"2018-05-07","cited_by_count":22},{"id":"1810.11530","title":"Automatic differentiation in ML: Where we are and where we should be going","created":"2018-10-26","cited_by_count":20},{"id":"2001.02381","title":"Learning to Zoom-in via Learning to Zoom-out: Real-world Super-resolution by Generating and Adapting Degradation","created":"2020-01-08","cited_by_count":10}],"next_cursor":null}