{"list":"other","total":61,"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":"1912.01703","title":"PyTorch: An Imperative Style, High-Performance Deep Learning Library","created":"2019-12-03","cited_by_count":20305},{"id":"2103.14030","title":"Swin Transformer: Hierarchical Vision Transformer using Shifted Windows","created":"2021-03-25","cited_by_count":12139},{"id":"2002.05709","title":"A Simple Framework for Contrastive Learning of Visual Representations","created":"2020-02-13","cited_by_count":12049},{"id":"1801.03924","title":"The Unreasonable Effectiveness of Deep Features as a Perceptual Metric","created":"2018-01-11","cited_by_count":11177},{"id":"1807.03748","title":"Representation Learning with Contrastive Predictive Coding","created":"2018-07-10","cited_by_count":7777},{"id":"1911.05722","title":"Momentum Contrast for Unsupervised Visual Representation Learning","created":"2019-11-13","cited_by_count":7513},{"id":"1608.03983","title":"SGDR: Stochastic Gradient Descent with Warm Restarts","created":"2016-08-13","cited_by_count":5045},{"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":"2004.11362","title":"Supervised Contrastive Learning","created":"2020-04-23","cited_by_count":2829},{"id":"2003.04297","title":"Improved Baselines with Momentum Contrastive Learning","created":"2020-03-09","cited_by_count":2287},{"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":"1707.02921","title":"Enhanced Deep Residual Networks for Single Image Super-Resolution","created":"2017-07-10","cited_by_count":1552},{"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":"1805.07836","title":"Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels","created":"2018-05-20","cited_by_count":997},{"id":"1906.07413","title":"Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss","created":"2019-06-18","cited_by_count":989},{"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":"2103.14006","title":"Designing a Practical Degradation Model for Deep Blind Image Super-Resolution","created":"2021-03-25","cited_by_count":529},{"id":"2205.04437","title":"Activating More Pixels in Image Super-Resolution Transformer","created":"2022-05-09","cited_by_count":438},{"id":"1902.06068","title":"Deep Learning for Image Super-resolution: A Survey","created":"2019-02-16","cited_by_count":369},{"id":"1510.04389","title":"Sketch-based Manga Retrieval using Manga109 Dataset","created":"2015-10-15","cited_by_count":329},{"id":"1612.02295","title":"Large-Margin Softmax Loss for Convolutional Neural Networks","created":"2016-12-07","cited_by_count":296},{"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":"1712.06116","title":"Learning a Single Convolutional Super-Resolution Network for Multiple Degradations","created":"2017-12-17","cited_by_count":228},{"id":"1904.03377","title":"Blind Super-Resolution With Iterative Kernel Correction","created":"2019-04-06","cited_by_count":203},{"id":"2104.00416","title":"Unsupervised Degradation Representation Learning for Blind Super-Resolution","created":"2021-04-01","cited_by_count":177},{"id":"1909.06581","title":"Blind Super-Resolution Kernel Estimation using an Internal-GAN","created":"2019-09-14","cited_by_count":175},{"id":"2007.11498","title":"CrossTransformers: spatially-aware few-shot transfer","created":"2020-07-22","cited_by_count":174},{"id":"1711.10703","title":"FSRNet: End-to-End Learning Face Super-Resolution with Facial Priors","created":"2017-11-29","cited_by_count":170},{"id":"2203.06697","title":"Efficient Long-Range Attention Network for Image Super-resolution","created":"2022-03-13","cited_by_count":151},{"id":"2108.11084","title":"Transformer for Single Image Super-Resolution","created":"2021-08-25","cited_by_count":150},{"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":"1911.07850","title":"Frequency Separation for Real-World Super-Resolution","created":"2019-11-18","cited_by_count":115},{"id":"1803.05598","title":"Large Margin Deep Networks for Classification","created":"2018-03-15","cited_by_count":112},{"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":"2002.11397","title":"Unpaired Image Super-Resolution using Pseudo-Supervision","created":"2020-02-26","cited_by_count":67},{"id":"2103.02368","title":"Real-World Single Image Super-Resolution: A Brief Review","created":"2021-03-03","cited_by_count":65},{"id":"2112.10175","title":"On Efficient Transformer-Based Image Pre-training for Low-Level Vision","created":"2021-12-19","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":"1712.02765","title":"Super-FAN: Integrated facial landmark localization and super-resolution of real-world low resolution faces in arbitrary poses with GANs","created":"2017-12-07","cited_by_count":55},{"id":"2007.12928","title":"Video Super Resolution Based on Deep Learning: A Comprehensive Survey","created":"2020-07-25","cited_by_count":54},{"id":"2202.07508","title":"Deep Constrained Least Squares for Blind Image Super-Resolution","created":"2022-02-15","cited_by_count":47},{"id":"2012.08103","title":"KOALAnet: Blind Super-Resolution using Kernel-Oriented Adaptive Local Adjustment","created":"2020-12-15","cited_by_count":37},{"id":"1708.03132","title":"Attention-Aware Face Hallucination via Deep Reinforcement Learning","created":"2017-08-10","cited_by_count":33},{"id":"2101.03749","title":"Deep Learning-based Face Super-Resolution: A Survey","created":"2021-01-11","cited_by_count":32},{"id":"2107.00986","title":"Blind Image Super-resolution with Elaborate Degradation Modeling on Noise and Kernel","created":"2021-07-02","cited_by_count":27},{"id":"2205.04910","title":"A Closer Look at Blind Super-Resolution: Degradation Models, Baselines, and Performance Upper Bounds","created":"2022-05-10","cited_by_count":22},{"id":"2105.06878","title":"End-to-end Alternating Optimization for Blind Super Resolution","created":"2021-05-14","cited_by_count":17},{"id":"2205.02031","title":"Self-Supervised Super-Resolution for Multi-Exposure Push-Frame Satellites","created":"2022-05-04","cited_by_count":8},{"id":"1809.06420","title":"Toward Bridging the Simulated-to-Real Gap: Benchmarking Super-Resolution on Real Data","created":"2018-09-17","cited_by_count":7},{"id":"2104.03926","title":"Conditional Hyper-Network for Blind Super-Resolution with Multiple Degradations","created":"2021-04-08","cited_by_count":7},{"id":"2101.11058","title":"Supervised Momentum Contrastive Learning for Few-Shot Classification","created":"2021-01-26","cited_by_count":6},{"id":"2004.00843","title":"Robust Single-Image Super-Resolution via CNNs and TV-TV Minimization","created":"2020-04-02","cited_by_count":4},{"id":"2110.14638","title":"Improving Super-Resolution Performance using Meta-Attention Layers","created":"2021-10-27","cited_by_count":2}],"next_cursor":null}