{"list":"other","total":53,"rows":[{"id":"1810.04805","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","created":"2018-10-11","cited_by_count":40399},{"id":"1412.6980","title":"Adam: A Method for Stochastic Optimization","created":"2014-12-22","cited_by_count":37240},{"id":"2005.14165","title":"Language Models are Few-Shot Learners","created":"2020-05-28","cited_by_count":32419},{"id":"2010.11929","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","created":"2020-10-22","cited_by_count":30186},{"id":"1801.03924","title":"The Unreasonable Effectiveness of Deep Features as a Perceptual Metric","created":"2018-01-11","cited_by_count":11177},{"id":"2111.06377","title":"Masked Autoencoders Are Scalable Vision Learners","created":"2021-11-11","cited_by_count":7403},{"id":"2106.08254","title":"BEiT: BERT Pre-Training of Image Transformers","created":"2021-06-15","cited_by_count":2375},{"id":"2108.10257","title":"SwinIR: Image Restoration Using Swin Transformer","created":"2021-08-23","cited_by_count":1887},{"id":"1905.00414","title":"Similarity of Neural Network Representations Revisited","created":"2019-05-01","cited_by_count":1755},{"id":"2111.09881","title":"Restormer: Efficient Transformer for High-Resolution Image Restoration","created":"2021-11-18","cited_by_count":1627},{"id":"1707.02921","title":"Enhanced Deep Residual Networks for Single Image Super-Resolution","created":"2017-07-10","cited_by_count":1552},{"id":"1604.07379","title":"Context Encoders: Feature Learning by Inpainting","created":"2016-04-25","cited_by_count":1428},{"id":"1608.03981","title":"Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising","created":"2016-08-13","cited_by_count":1358},{"id":"2111.09886","title":"SimMIM: A Simple Framework for Masked Image Modeling","created":"2021-11-18","cited_by_count":1153},{"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":"2102.02808","title":"Multi-Stage Progressive Image Restoration","created":"2021-02-04","cited_by_count":816},{"id":"2108.08810","title":"Do Vision Transformers See Like Convolutional Neural Networks?","created":"2021-08-19","cited_by_count":675},{"id":"1710.04026","title":"FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoising","created":"2017-10-11","cited_by_count":510},{"id":"1811.10980","title":"Noise2Void - Learning Denoising from Single Noisy Images","created":"2018-11-27","cited_by_count":364},{"id":"2006.04139","title":"Learning Texture Transformer Network for Image Super-Resolution","created":"2020-06-07","cited_by_count":347},{"id":"1807.04686","title":"Toward Convolutional Blind Denoising of Real Photographs","created":"2018-07-12","cited_by_count":274},{"id":"1811.11127","title":"Unprocessing Images for Learned Raw Denoising","created":"2018-11-27","cited_by_count":250},{"id":"1708.02209","title":"MemNet: A Persistent Memory Network for Image Restoration","created":"2017-08-07","cited_by_count":244},{"id":"1903.10082","title":"Residual Non-local Attention Networks for Image Restoration","created":"2019-03-24","cited_by_count":236},{"id":"1707.01313","title":"Benchmarking Denoising Algorithms with Real Photographs","created":"2017-07-05","cited_by_count":209},{"id":"1904.03377","title":"Blind Super-Resolution With Iterative Kernel Correction","created":"2019-04-06","cited_by_count":203},{"id":"1508.02848","title":"Trainable Nonlinear Reaction Diffusion: A Flexible Framework for Fast and Effective Image Restoration","created":"2015-08-12","cited_by_count":195},{"id":"1812.10477","title":"Residual Dense Network for Image Restoration","created":"2018-12-25","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":"1904.07396","title":"Real Image Denoising with Feature Attention","created":"2019-04-16","cited_by_count":176},{"id":"1806.02919","title":"Non-Local Recurrent Network for Image Restoration","created":"2018-06-07","cited_by_count":160},{"id":"2003.12751","title":"A Physics-based Noise Formation Model for Extreme Low-light Raw Denoising","created":"2020-03-28","cited_by_count":138},{"id":"2011.11036","title":"Interpreting Super-Resolution Networks with Local Attribution Maps","created":"2020-11-22","cited_by_count":136},{"id":"1809.00437","title":"Unsupervised Image Super-Resolution using Cycle-in-Cycle Generative Adversarial Networks","created":"2018-09-03","cited_by_count":133},{"id":"1908.11314","title":"Variational Denoising Network: Toward Blind Noise Modeling and Removal","created":"2019-08-29","cited_by_count":117},{"id":"2007.05946","title":"Dual Adversarial Network: Toward Real-world Noise Removal and Noise Generation","created":"2020-07-12","cited_by_count":101},{"id":"1810.12575","title":"Neural Nearest Neighbors Networks","created":"2018-10-30","cited_by_count":98},{"id":"2107.03055","title":"Blind Image Super-Resolution: A Survey and Beyond","created":"2021-07-07","cited_by_count":93},{"id":"2211.13654","title":"Cross Aggregation Transformer for Image Restoration","created":"2022-11-24","cited_by_count":92},{"id":"2203.13278","title":"Practical Blind Image Denoising via Swin-Conv-UNet and Data Synthesis","created":"2022-03-24","cited_by_count":86},{"id":"2210.01427","title":"Accurate Image Restoration with Attention Retractable Transformer","created":"2022-10-04","cited_by_count":85},{"id":"2012.15028","title":"NBNet: Noise Basis Learning for Image Denoising with Subspace Projection","created":"2020-12-30","cited_by_count":77},{"id":"2207.08494","title":"Rethinking Alignment in Video Super-Resolution Transformers","created":"2022-07-18","cited_by_count":71},{"id":"2112.12089","title":"Reflash Dropout in Image Super-Resolution","created":"2021-12-22","cited_by_count":41},{"id":"1611.06757","title":"Non-Local Color Image Denoising with Convolutional Neural Networks","created":"2016-11-21","cited_by_count":39},{"id":"2303.06440","title":"Xformer: Hybrid X-Shaped Transformer for Image Denoising","created":"2023-03-11","cited_by_count":37},{"id":"1803.05215","title":"Deep Image Demosaicking using a Cascade of Convolutional Residual Denoising Networks","created":"2018-03-14","cited_by_count":31},{"id":"2303.06373","title":"Recursive Generalization Transformer for Image Super-Resolution","created":"2023-03-11","cited_by_count":29},{"id":"2011.15002","title":"Image Quality Assessment for Perceptual Image Restoration: A New Dataset, Benchmark and Metric","created":"2020-11-30","cited_by_count":26},{"id":"2205.07019","title":"Evaluating the Generalization Ability of Super-Resolution Networks","created":"2022-05-14","cited_by_count":18},{"id":"1906.04809","title":"Suppressing Model Overfitting for Image Super-Resolution Networks","created":"2019-06-11","cited_by_count":9},{"id":"1708.00159","title":"Image Denoising via CNNs: An Adversarial Approach","created":"2017-08-01","cited_by_count":7}],"next_cursor":null}