{"list":"other","total":52,"rows":[{"id":"1706.03762","title":"Attention Is All You Need","created":"2017-06-12","cited_by_count":60361},{"id":"1512.03385","title":"Deep Residual Learning for Image Recognition","created":"2015-12-10","cited_by_count":52096},{"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":"1409.1556","title":"Very Deep Convolutional Networks for Large-Scale Image Recognition","created":"2014-09-04","cited_by_count":15364},{"id":"2103.14030","title":"Swin Transformer: Hierarchical Vision Transformer using Shifted Windows","created":"2021-03-25","cited_by_count":12139},{"id":"2005.12872","title":"End-to-End Object Detection with Transformers","created":"2020-05-26","cited_by_count":7414},{"id":"1502.03167","title":"Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift","created":"2015-02-11","cited_by_count":7005},{"id":"2012.12877","title":"Training data-efficient image transformers & distillation through attention","created":"2020-12-23","cited_by_count":4586},{"id":"2010.04159","title":"Deformable DETR: Deformable Transformers for End-to-End Object Detection","created":"2020-10-08","cited_by_count":3422},{"id":"1505.07818","title":"Domain-Adversarial Training of Neural Networks","created":"2015-05-28","cited_by_count":3044},{"id":"2102.12122","title":"Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions","created":"2021-02-24","cited_by_count":1962},{"id":"1409.7495","title":"Unsupervised Domain Adaptation by Backpropagation","created":"2014-09-26","cited_by_count":1917},{"id":"1811.12231","title":"ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness","created":"2018-11-29","cited_by_count":1432},{"id":"2012.15840","title":"Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers","created":"2020-12-31","cited_by_count":1369},{"id":"1812.01754","title":"Moment Matching for Multi-Source Domain Adaptation","created":"2018-12-04","cited_by_count":1331},{"id":"1702.05464","title":"Adversarial Discriminative Domain Adaptation","created":"2017-02-17","cited_by_count":1311},{"id":"1706.07522","title":"Deep Hashing Network for Unsupervised Domain Adaptation","created":"2017-06-22","cited_by_count":1188},{"id":"2103.15808","title":"CvT: Introducing Convolutions to Vision Transformers","created":"2021-03-29","cited_by_count":947},{"id":"1712.02560","title":"Maximum Classifier Discrepancy for Unsupervised Domain Adaptation","created":"2017-12-07","cited_by_count":683},{"id":"2105.10497","title":"Intriguing Properties of Vision Transformers","created":"2021-05-21","cited_by_count":446},{"id":"2011.14503","title":"End-to-End Video Instance Segmentation with Transformers","created":"2020-11-30","cited_by_count":443},{"id":"2108.13624","title":"Towards Out-Of-Distribution Generalization: A Survey","created":"2021-08-31","cited_by_count":436},{"id":"1812.02707","title":"Video Action Transformer Network","created":"2018-12-06","cited_by_count":316},{"id":"2102.00719","title":"Video Transformer Network","created":"2021-02-01","cited_by_count":272},{"id":"2004.04393","title":"Universal Source-Free Domain Adaptation","created":"2020-04-09","cited_by_count":220},{"id":"2002.07953","title":"Universal Domain Adaptation through Self Supervision","created":"2020-02-19","cited_by_count":172},{"id":"1804.10427","title":"Open Set Domain Adaptation by Backpropagation","created":"2018-04-27","cited_by_count":150},{"id":"2109.06165","title":"CDTrans: Cross-domain Transformer for Unsupervised Domain Adaptation","created":"2021-09-13","cited_by_count":129},{"id":"1808.04205","title":"Partial Adversarial Domain Adaptation","created":"2018-08-10","cited_by_count":127},{"id":"2106.03650","title":"Shuffle Transformer: Rethinking Spatial Shuffle for Vision Transformer","created":"2021-06-07","cited_by_count":115},{"id":"2105.07197","title":"Are Convolutional Neural Networks or Transformers more like human vision?","created":"2021-05-15","cited_by_count":109},{"id":"2010.08982","title":"Federated Unsupervised Representation Learning","created":"2020-10-18","cited_by_count":93},{"id":"2104.03344","title":"OVANet: One-vs-All Network for Universal Domain Adaptation","created":"2021-04-07","cited_by_count":92},{"id":"2105.03818","title":"Heterogeneous Risk Minimization","created":"2021-05-09","cited_by_count":90},{"id":"1707.07901","title":"Partial Transfer Learning with Selective Adversarial Networks","created":"2017-07-25","cited_by_count":85},{"id":"2108.05988","title":"TVT: Transferable Vision Transformer for Unsupervised Domain Adaptation","created":"2021-08-12","cited_by_count":81},{"id":"2007.12360","title":"On the Effectiveness of Image Rotation for Open Set Domain Adaptation","created":"2020-07-24","cited_by_count":72},{"id":"2006.16765","title":"Federated Mutual Learning","created":"2020-06-27","cited_by_count":71},{"id":"2204.07683","title":"Safe Self-Refinement for Transformer-based Domain Adaptation","created":"2022-04-16","cited_by_count":61},{"id":"2210.17067","title":"Unified Optimal Transport Framework for Universal Domain Adaptation","created":"2022-10-31","cited_by_count":57},{"id":"2003.02541","title":"A Balanced and Uncertainty-aware Approach for Partial Domain Adaptation","created":"2020-03-05","cited_by_count":55},{"id":"2209.05227","title":"DUET: A Tuning-Free Device-Cloud Collaborative Parameters Generation Framework for Efficient Device Model Generalization","created":"2022-09-12","cited_by_count":35},{"id":"2302.07335","title":"Intelligent Model Update Strategy for Sequential Recommendation","created":"2023-02-14","cited_by_count":25},{"id":"2110.12425","title":"Kernelized Heterogeneous Risk Minimization","created":"2021-10-24","cited_by_count":21},{"id":"2110.00726","title":"Domain-Specific Bias Filtering for Single Labeled Domain Generalization","created":"2021-10-02","cited_by_count":18},{"id":"2210.15909","title":"Subsidiary Prototype Alignment for Universal Domain Adaptation","created":"2022-10-28","cited_by_count":17},{"id":"2110.01438","title":"Instrumental Variable-Driven Domain Generalization with Unobserved Confounders","created":"2021-10-04","cited_by_count":7},{"id":"2001.05071","title":"A Sample Selection Approach for Universal Domain Adaptation","created":"2020-01-14","cited_by_count":5},{"id":"2303.06679","title":"RotoGBML: Towards Out-of-Distribution Generalization for Gradient-Based Meta-Learning","created":"2023-03-12","cited_by_count":5},{"id":"2202.13310","title":"Attention-based Cross-Layer Domain Alignment for Unsupervised Domain Adaptation","created":"2022-02-27","cited_by_count":4},{"id":"2206.11054","title":"S2RL: Do We Really Need to Perceive All States in Deep Multi-Agent Reinforcement Learning?","created":"2022-06-20","cited_by_count":3},{"id":"1403.1353","title":"Collaborative Representation for Classification, Sparse or Non-sparse?","created":"2014-03-06","cited_by_count":1}],"next_cursor":null}