{"list":"other","total":55,"rows":[{"id":"1706.03762","title":"Attention Is All You Need","created":"2017-06-12","cited_by_count":60361},{"id":"1412.6980","title":"Adam: A Method for Stochastic Optimization","created":"2014-12-22","cited_by_count":37240},{"id":"1912.01703","title":"PyTorch: An Imperative Style, High-Performance Deep Learning Library","created":"2019-12-03","cited_by_count":20305},{"id":"1901.05103","title":"DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation","created":"2019-01-16","cited_by_count":2689},{"id":"2006.09661","title":"Implicit Neural Representations with Periodic Activation Functions","created":"2020-06-17","cited_by_count":2238},{"id":"1812.03828","title":"Occupancy Networks: Learning 3D Reconstruction in Function Space","created":"2018-12-10","cited_by_count":2041},{"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":"2112.05131","title":"Plenoxels: Radiance Fields without Neural Networks","created":"2021-12-09","cited_by_count":1613},{"id":"1707.02921","title":"Enhanced Deep Residual Networks for Single Image Super-Resolution","created":"2017-07-10","cited_by_count":1552},{"id":"1806.08734","title":"On the Spectral Bias of Neural Networks","created":"2018-06-22","cited_by_count":1341},{"id":"1807.02758","title":"Image Super-Resolution Using Very Deep Residual Channel Attention Networks","created":"2018-07-08","cited_by_count":1264},{"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":"2106.12423","title":"Alias-Free Generative Adversarial Networks","created":"2021-06-23","cited_by_count":1184},{"id":"2107.10833","title":"Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data","created":"2021-07-22","cited_by_count":970},{"id":"2007.11571","title":"Neural Sparse Voxel Fields","created":"2020-07-22","cited_by_count":959},{"id":"1511.04587","title":"Accurate Image Super-Resolution Using Very Deep Convolutional Networks","created":"2015-11-14","cited_by_count":903},{"id":"1906.01618","title":"Scene Representation Networks: Continuous 3D-Structure-Aware Neural Scene Representations","created":"2019-06-04","cited_by_count":864},{"id":"2010.07492","title":"NeRF++: Analyzing and Improving Neural Radiance Fields","created":"2020-10-15","cited_by_count":861},{"id":"1802.08797","title":"Residual Dense Network for Image Super-Resolution","created":"2018-02-24","cited_by_count":799},{"id":"2011.12100","title":"GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fields","created":"2020-11-24","cited_by_count":785},{"id":"2003.04618","title":"Convolutional Occupancy Networks","created":"2020-03-10","cited_by_count":761},{"id":"1912.07372","title":"Differentiable Volumetric Rendering: Learning Implicit 3D Representations without 3D Supervision","created":"2019-12-16","cited_by_count":700},{"id":"2007.02442","title":"GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis","created":"2020-07-05","cited_by_count":689},{"id":"2012.09161","title":"Learning Continuous Image Representation with Local Implicit Image Function","created":"2020-12-16","cited_by_count":552},{"id":"2004.05498","title":"FDA: Fourier Domain Adaptation for Semantic Segmentation","created":"2020-04-11","cited_by_count":541},{"id":"1704.03915","title":"Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution","created":"2017-04-12","cited_by_count":453},{"id":"1812.01024","title":"DeepVoxels: Learning Persistent 3D Feature Embeddings","created":"2018-12-03","cited_by_count":405},{"id":"1608.00367","title":"Accelerating the Super-Resolution Convolutional Neural Network","created":"2016-08-01","cited_by_count":394},{"id":"1902.06068","title":"Deep Learning for Image Super-resolution: A Survey","created":"2019-02-16","cited_by_count":369},{"id":"1610.07629","title":"A Learned Representation For Artistic Style","created":"2016-10-24","cited_by_count":341},{"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":"1708.02209","title":"MemNet: A Persistent Memory Network for Image Restoration","created":"2017-08-07","cited_by_count":244},{"id":"1803.02735","title":"Deep Back-Projection Networks For Super-Resolution","created":"2018-03-07","cited_by_count":225},{"id":"1905.07259","title":"Texture Fields: Learning Texture Representations in Function Space","created":"2019-05-17","cited_by_count":210},{"id":"2012.12821","title":"Focal Frequency Loss for Image Reconstruction and Synthesis","created":"2020-12-23","cited_by_count":206},{"id":"1805.07071","title":"Multi-level Wavelet-CNN for Image Restoration","created":"2018-05-18","cited_by_count":184},{"id":"1903.00875","title":"Meta-SR: A Magnification-Arbitrary Network for Super-Resolution","created":"2019-03-03","cited_by_count":181},{"id":"1903.09814","title":"Feedback Network for Image Super-Resolution","created":"2019-03-23","cited_by_count":155},{"id":"1710.01992","title":"Fast and Accurate Image Super-Resolution with Deep Laplacian Pyramid Networks","created":"2017-10-04","cited_by_count":138},{"id":"2111.08918","title":"Local Texture Estimator for Implicit Representation Function","created":"2021-11-17","cited_by_count":127},{"id":"1911.07850","title":"Frequency Separation for Real-World Super-Resolution","created":"2019-11-18","cited_by_count":115},{"id":"1506.03767","title":"Spectral Representations for Convolutional Neural Networks","created":"2015-06-11","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":"2004.03791","title":"Learning A Single Network for Scale-Arbitrary Super-Resolution","created":"2020-04-08","cited_by_count":66},{"id":"2104.03963","title":"InfinityGAN: Towards Infinite-Pixel Image Synthesis","created":"2021-04-08","cited_by_count":61},{"id":"2103.12716","title":"UltraSR: Spatial Encoding is a Missing Key for Implicit Image Function-based Arbitrary-Scale Super-Resolution","created":"2021-03-23","cited_by_count":57},{"id":"1804.02900","title":"A Fully Progressive Approach to Single-Image Super-Resolution","created":"2018-04-09","cited_by_count":46},{"id":"2112.06536","title":"SphereSR: 360{\\deg} Image Super-Resolution with Arbitrary Projection via Continuous Spherical Image Representation","created":"2021-12-13","cited_by_count":33},{"id":"2104.10325","title":"SRWarp: Generalized Image Super-Resolution under Arbitrary Transformation","created":"2021-04-21","cited_by_count":27},{"id":"2210.05171","title":"Deep Fourier Up-Sampling","created":"2022-10-11","cited_by_count":27},{"id":"1611.06345","title":"Beyond Deep Residual Learning for Image Restoration: Persistent Homology-Guided Manifold Simplification","created":"2016-11-19","cited_by_count":22},{"id":"2108.01285","title":"Toward Spatially Unbiased Generative Models","created":"2021-08-03","cited_by_count":17},{"id":"2205.13524","title":"PREF: Phasorial Embedding Fields for Compact Neural Representations","created":"2022-05-26","cited_by_count":10}],"next_cursor":null}