{"list":"other","total":53,"rows":[{"id":"1706.03762","title":"Attention Is All You Need","created":"2017-06-12","cited_by_count":60361},{"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":"1912.01703","title":"PyTorch: An Imperative Style, High-Performance Deep Learning Library","created":"2019-12-03","cited_by_count":20305},{"id":"1608.03983","title":"SGDR: Stochastic Gradient Descent with Warm Restarts","created":"2016-08-13","cited_by_count":5045},{"id":"2312.00752","title":"Mamba: Linear-Time Sequence Modeling with Selective State Spaces","created":"2023-12-01","cited_by_count":4718},{"id":"1704.04861","title":"MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications","created":"2017-04-17","cited_by_count":3845},{"id":"1606.08415","title":"Gaussian Error Linear Units (GELUs)","created":"2016-06-27","cited_by_count":3502},{"id":"1412.3555","title":"Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling","created":"2014-12-11","cited_by_count":2367},{"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":"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":"1611.01578","title":"Neural Architecture Search with Reinforcement Learning","created":"2016-11-05","cited_by_count":1499},{"id":"2111.09883","title":"Swin Transformer V2: Scaling Up Capacity and Resolution","created":"2021-11-18","cited_by_count":1228},{"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":"1707.07012","title":"Learning Transferable Architectures for Scalable Image Recognition","created":"2017-07-21","cited_by_count":1101},{"id":"2401.09417","title":"Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model","created":"2024-01-17","cited_by_count":1056},{"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":"2102.02808","title":"Multi-Stage Progressive Image Restoration","created":"2021-02-04","cited_by_count":816},{"id":"2403.14608","title":"Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey","created":"2024-03-21","cited_by_count":656},{"id":"2101.03697","title":"RepVGG: Making VGG-style ConvNets Great Again","created":"2021-01-11","cited_by_count":522},{"id":"2205.04437","title":"Activating More Pixels in Image Super-Resolution Transformer","created":"2022-05-09","cited_by_count":438},{"id":"2108.05054","title":"Rethinking Coarse-to-Fine Approach in Single Image Deblurring","created":"2021-08-11","cited_by_count":326},{"id":"2201.02973","title":"MAXIM: Multi-Axis MLP for Image Processing","created":"2022-01-09","cited_by_count":302},{"id":"1903.10258","title":"MetaPruning: Meta Learning for Automatic Neural Network Channel Pruning","created":"2019-03-25","cited_by_count":227},{"id":"1909.11856","title":"Lightweight Image Super-Resolution with Information Multi-distillation Network","created":"2019-09-26","cited_by_count":227},{"id":"2208.06677","title":"Adan: Adaptive Nesterov Momentum Algorithm for Faster Optimizing Deep Models","created":"2022-08-13","cited_by_count":174},{"id":"2303.00748","title":"Efficient and Explicit Modelling of Image Hierarchies for Image Restoration","created":"2023-03-01","cited_by_count":145},{"id":"2303.03667","title":"Run, Don't Walk: Chasing Higher FLOPS for Faster Neural Networks","created":"2023-03-07","cited_by_count":130},{"id":"2009.11551","title":"Residual Feature Distillation Network for Lightweight Image Super-Resolution","created":"2020-09-24","cited_by_count":122},{"id":"2103.06255","title":"Involution: Inverting the Inherence of Convolution for Visual Recognition","created":"2021-03-10","cited_by_count":99},{"id":"2103.13425","title":"Diverse Branch Block: Building a Convolution as an Inception-like Unit","created":"2021-03-24","cited_by_count":87},{"id":"2112.04491","title":"Improving Image Restoration by Revisiting Global Information Aggregation","created":"2021-12-08","cited_by_count":61},{"id":"2003.13683","title":"DHP: Differentiable Meta Pruning via HyperNetworks","created":"2020-03-30","cited_by_count":57},{"id":"2302.13800","title":"Spatially-Adaptive Feature Modulation for Efficient Image Super-Resolution","created":"2023-02-27","cited_by_count":57},{"id":"2205.07514","title":"Residual Local Feature Network for Efficient Super-Resolution","created":"2022-05-16","cited_by_count":53},{"id":"2211.11436","title":"N-Gram in Swin Transformers for Efficient Lightweight Image Super-Resolution","created":"2022-11-21","cited_by_count":46},{"id":"2205.05996","title":"Blueprint Separable Residual Network for Efficient Image Super-Resolution","created":"2022-05-12","cited_by_count":44},{"id":"2404.09790","title":"NTIRE 2024 Challenge on Image Super-Resolution (x4): Methods and Results","created":"2024-04-15","cited_by_count":40},{"id":"2205.12551","title":"Masked Jigsaw Puzzle: A Versatile Position Embedding for Vision Transformers","created":"2022-05-25","cited_by_count":33},{"id":"2204.08397","title":"Fast and Memory-Efficient Network Towards Efficient Image Super-Resolution","created":"2022-04-18","cited_by_count":27},{"id":"2105.09750","title":"Anchor-based Plain Net for Mobile Image Super-Resolution","created":"2021-05-20","cited_by_count":24},{"id":"2311.12770","title":"Swift Parameter-free Attention Network for Efficient Super-Resolution","created":"2023-11-21","cited_by_count":24},{"id":"2311.17048","title":"Zero-shot Referring Expression Comprehension via Structural Similarity Between Images and Captions","created":"2023-11-28","cited_by_count":21},{"id":"2103.09404","title":"Collapsible Linear Blocks for Super-Efficient Super Resolution","created":"2021-03-17","cited_by_count":19},{"id":"2304.07018","title":"DIPNet: Efficiency Distillation and Iterative Pruning for Image Super-Resolution","created":"2023-04-14","cited_by_count":19},{"id":"2204.08759","title":"Edge-enhanced Feature Distillation Network for Efficient Super-Resolution","created":"2022-04-19","cited_by_count":18},{"id":"2006.16242","title":"The Heterogeneity Hypothesis: Finding Layer-Wise Differentiated Network Architectures","created":"2020-06-29","cited_by_count":13},{"id":"2304.06703","title":"Gated Multi-Resolution Transfer Network for Burst Restoration and Enhancement","created":"2023-04-13","cited_by_count":11},{"id":"2401.06426","title":"UPDP: A Unified Progressive Depth Pruner for CNN and Vision Transformer","created":"2024-01-12","cited_by_count":9},{"id":"2302.00252","title":"QLABGrad: a Hyperparameter-Free and Convergence-Guaranteed Scheme for Deep Learning","created":"2023-02-01","cited_by_count":6},{"id":"2111.13905","title":"AdaDM: Enabling Normalization for Image Super-Resolution","created":"2021-11-27","cited_by_count":5},{"id":"2112.00729","title":"Total-Body Low-Dose CT Image Denoising using Prior Knowledge Transfer Technique with Contrastive Regularization Mechanism","created":"2021-12-01","cited_by_count":3}],"next_cursor":null}