{"list":"other","total":82,"rows":[{"id":"1512.03385","title":"Deep Residual Learning for Image Recognition","created":"2015-12-10","cited_by_count":52096},{"id":"1801.03924","title":"The Unreasonable Effectiveness of Deep Features as a Perceptual Metric","created":"2018-01-11","cited_by_count":11177},{"id":"1502.03167","title":"Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift","created":"2015-02-11","cited_by_count":7005},{"id":"1608.06993","title":"Densely Connected Convolutional Networks","created":"2016-08-25","cited_by_count":6720},{"id":"1411.4038","title":"Fully Convolutional Networks for Semantic Segmentation","created":"2014-11-14","cited_by_count":4859},{"id":"1604.01685","title":"The Cityscapes Dataset for Semantic Urban Scene Understanding","created":"2016-04-06","cited_by_count":4444},{"id":"1603.08155","title":"Perceptual Losses for Real-Time Style Transfer and Super-Resolution","created":"2016-03-27","cited_by_count":2694},{"id":"1905.02244","title":"Searching for MobileNetV3","created":"2019-05-06","cited_by_count":1994},{"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":"1611.01578","title":"Neural Architecture Search with Reinforcement Learning","created":"2016-11-05","cited_by_count":1499},{"id":"1703.06211","title":"Deformable Convolutional Networks","created":"2017-03-17","cited_by_count":1488},{"id":"1506.02025","title":"Spatial Transformer Networks","created":"2015-06-05","cited_by_count":1429},{"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":"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":"1807.11164","title":"ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design","created":"2018-07-30","cited_by_count":1136},{"id":"1709.02371","title":"PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume","created":"2017-09-07","cited_by_count":968},{"id":"2004.05439","title":"Meta-Learning in Neural Networks: A Survey","created":"2020-04-11","cited_by_count":950},{"id":"1511.04587","title":"Accurate Image Super-Resolution Using Very Deep Convolutional Networks","created":"2015-11-14","cited_by_count":903},{"id":"1612.01925","title":"FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks","created":"2016-12-06","cited_by_count":784},{"id":"1811.11168","title":"Deformable ConvNets v2: More Deformable, Better Results","created":"2018-11-27","cited_by_count":670},{"id":"1709.01686","title":"BranchyNet: Fast Inference via Early Exiting from Deep Neural Networks","created":"2017-09-06","cited_by_count":596},{"id":"1711.06077","title":"The Perception-Distortion Tradeoff","created":"2017-11-16","cited_by_count":587},{"id":"1905.02716","title":"EDVR: Video Restoration with Enhanced Deformable Convolutional Networks","created":"2019-05-07","cited_by_count":494},{"id":"1611.00850","title":"Optical Flow Estimation using a Spatial Pyramid Network","created":"2016-11-03","cited_by_count":424},{"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":"1711.09485","title":"SkipNet: Learning Dynamic Routing in Convolutional Networks","created":"2017-11-26","cited_by_count":332},{"id":"2012.02181","title":"BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond","created":"2020-12-03","cited_by_count":304},{"id":"1712.06087","title":"\"Zero-Shot\" Super-Resolution using Deep Internal Learning","created":"2017-12-17","cited_by_count":286},{"id":"1803.08664","title":"Fast, Accurate, and Lightweight Super-Resolution with Cascading Residual Network","created":"2018-03-23","cited_by_count":264},{"id":"1704.08545","title":"ICNet for Real-Time Semantic Segmentation on High-Resolution Images","created":"2017-04-27","cited_by_count":261},{"id":"1812.02898","title":"TDAN: Temporally Deformable Alignment Network for Video Super-Resolution","created":"2018-12-07","cited_by_count":226},{"id":"1904.03377","title":"Blind Super-Resolution With Iterative Kernel Correction","created":"2019-04-06","cited_by_count":203},{"id":"1909.06581","title":"Blind Super-Resolution Kernel Estimation using an Internal-GAN","created":"2019-09-14","cited_by_count":175},{"id":"1801.04590","title":"Frame-Recurrent Video Super-Resolution","created":"2018-01-14","cited_by_count":160},{"id":"1808.03344","title":"Deep Learning for Single Image Super-Resolution: A Brief Review","created":"2018-08-09","cited_by_count":158},{"id":"2012.00739","title":"GLEAN: Generative Latent Bank for Large-Factor Image Super-Resolution","created":"2020-12-01","cited_by_count":158},{"id":"1704.02738","title":"Detail-revealing Deep Video Super-resolution","created":"2017-04-10","cited_by_count":148},{"id":"1810.05331","title":"Dynamic Channel Pruning: Feature Boosting and Suppression","created":"2018-10-12","cited_by_count":129},{"id":"1803.09454","title":"Fast and Accurate Single Image Super-Resolution via Information Distillation Network","created":"2018-03-26","cited_by_count":121},{"id":"2008.10174","title":"Fast Bi-layer Neural Synthesis of One-Shot Realistic Head Avatars","created":"2020-08-24","cited_by_count":118},{"id":"2002.12213","title":"Meta-Transfer Learning for Zero-Shot Super-Resolution","created":"2020-02-27","cited_by_count":116},{"id":"2003.13081","title":"Structure-Preserving Super Resolution with Gradient Guidance","created":"2020-03-29","cited_by_count":116},{"id":"2008.06402","title":"SPINN: Synergistic Progressive Inference of Neural Networks over Device and Cloud","created":"2020-08-14","cited_by_count":112},{"id":"2007.08668","title":"BRP-NAS: Prediction-based NAS using GCNs","created":"2020-07-16","cited_by_count":111},{"id":"2005.05650","title":"Invertible Image Rescaling","created":"2020-05-12","cited_by_count":92},{"id":"2002.11616","title":"Zooming Slow-Mo: Fast and Accurate One-Stage Space-Time Video Super-Resolution","created":"2020-02-26","cited_by_count":91},{"id":"1805.12549","title":"Channel Gating Neural Networks","created":"2018-05-29","cited_by_count":80},{"id":"1910.06663","title":"AI Benchmark: All About Deep Learning on Smartphones in 2019","created":"2019-10-15","cited_by_count":77},{"id":"1810.10090","title":"NestDNN: Resource-Aware Multi-Tenant On-Device Deep Learning for Continuous Mobile Vision","created":"2018-10-23","cited_by_count":68},{"id":"1804.00389","title":"Low-Latency Video Semantic Segmentation","created":"2018-04-02","cited_by_count":61},{"id":"2007.12928","title":"Video Super Resolution Based on Deep Learning: A Comprehensive Survey","created":"2020-07-25","cited_by_count":54},{"id":"2011.04212","title":"PAMS: Quantized Super-Resolution via Parameterized Max Scale","created":"2020-11-09","cited_by_count":48},{"id":"1811.10515","title":"Deep Network Interpolation for Continuous Imagery Effect Transition","created":"2018-11-26","cited_by_count":47},{"id":"1801.03493","title":"Focus: Querying Large Video Datasets with Low Latency and Low Cost","created":"2018-01-10","cited_by_count":46},{"id":"2001.02905","title":"Fast Adaptation to Super-Resolution Networks via Meta-Learning","created":"2020-01-09","cited_by_count":46},{"id":"1703.02529","title":"NoScope: Optimizing Neural Network Queries over Video at Scale","created":"2017-03-07","cited_by_count":37},{"id":"1909.11409","title":"Efficient Residual Dense Block Search for Image Super-Resolution","created":"2019-09-25","cited_by_count":37},{"id":"1905.13536","title":"Scaling Video Analytics on Constrained Edge Nodes","created":"2019-05-24","cited_by_count":36},{"id":"1911.03624","title":"Natural and Realistic Single Image Super-Resolution with Explicit Natural Manifold Discrimination","created":"2019-11-09","cited_by_count":32},{"id":"2002.05022","title":"Best of Both Worlds: AutoML Codesign of a CNN and its Hardware Accelerator","created":"2020-02-11","cited_by_count":30},{"id":"2008.03997","title":"HAPI: Hardware-Aware Progressive Inference","created":"2020-08-10","cited_by_count":30},{"id":"1812.06378","title":"Efficient Super Resolution Using Binarized Neural Network","created":"2018-12-16","cited_by_count":29},{"id":"2006.16581","title":"Early Exit or Not: Resource-Efficient Blind Quality Enhancement for Compressed Images","created":"2020-06-30","cited_by_count":28},{"id":"1601.06748","title":"BOLA: Near-Optimal Bitrate Adaptation for Online Videos","created":"2016-01-25","cited_by_count":27},{"id":"1803.05900","title":"Toolflows for Mapping Convolutional Neural Networks on FPGAs: A Survey and Future Directions","created":"2018-03-15","cited_by_count":27},{"id":"1908.07985","title":"MobiSR: Efficient On-Device Super-Resolution through Heterogeneous Mobile Processors","created":"2019-08-21","cited_by_count":27},{"id":"2007.04356","title":"Journey Towards Tiny Perceptual Super-Resolution","created":"2020-07-08","cited_by_count":27},{"id":"1906.03951","title":"SCAN: A Scalable Neural Networks Framework Towards Compact and Efficient Models","created":"2019-05-27","cited_by_count":22},{"id":"1905.07346","title":"EmBench: Quantifying Performance Variations of Deep Neural Networks across Modern Commodity Devices","created":"2019-05-17","cited_by_count":21},{"id":"1803.04626","title":"Maintaining Natural Image Statistics with the Contextual Loss","created":"2018-03-13","cited_by_count":19},{"id":"1611.06453","title":"Fast Video Classification via Adaptive Cascading of Deep Models","created":"2016-11-20","cited_by_count":17},{"id":"1703.05393","title":"Convolutional Low-Resolution Fine-Grained Classification","created":"2017-03-15","cited_by_count":10},{"id":"1305.0510","title":"Probe and Adapt: Rate Adaptation for HTTP Video Streaming At Scale","created":"2013-05-01","cited_by_count":9},{"id":"1805.02482","title":"QARC: Video Quality Aware Rate Control for Real-Time Video Streaming via Deep Reinforcement Learning","created":"2018-05-07","cited_by_count":7},{"id":"2003.08872","title":"Across Scales & Across Dimensions: Temporal Super-Resolution using Deep Internal Learning","created":"2020-03-19","cited_by_count":7},{"id":"1711.01008","title":"Cost-Efficient and Robust On-Demand Video Transcoding Using Heterogeneous Cloud Services","created":"2017-11-03","cited_by_count":2},{"id":"1806.08616","title":"Deploying Deep Neural Networks in the Embedded Space","created":"2018-06-22","cited_by_count":1},{"id":"2001.02951","title":"Understanding video streaming algorithms in the wild","created":"2020-01-09","cited_by_count":1}],"next_cursor":null}