{"list":"other","total":63,"rows":[{"id":"1512.03385","title":"Deep Residual Learning for Image Recognition","created":"2015-12-10","cited_by_count":52096},{"id":"1912.01703","title":"PyTorch: An Imperative Style, High-Performance Deep Learning Library","created":"2019-12-03","cited_by_count":20305},{"id":"1503.02531","title":"Distilling the Knowledge in a Neural Network","created":"2015-03-09","cited_by_count":10324},{"id":"1710.09412","title":"mixup: Beyond Empirical Risk Minimization","created":"2017-10-25","cited_by_count":5183},{"id":"1505.07818","title":"Domain-Adversarial Training of Neural Networks","created":"2015-05-28","cited_by_count":3044},{"id":"1909.13719","title":"RandAugment: Practical automated data augmentation with a reduced search space","created":"2019-09-30","cited_by_count":2269},{"id":"2001.07685","title":"FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence","created":"2020-01-21","cited_by_count":2085},{"id":"1409.7495","title":"Unsupervised Domain Adaptation by Backpropagation","created":"2014-09-26","cited_by_count":1917},{"id":"1905.02249","title":"MixMatch: A Holistic Approach to Semi-Supervised Learning","created":"2019-05-06","cited_by_count":1391},{"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":"1607.01719","title":"Deep CORAL: Correlation Alignment for Deep Domain Adaptation","created":"2016-07-06","cited_by_count":1200},{"id":"1706.07522","title":"Deep Hashing Network for Unsupervised Domain Adaptation","created":"2017-06-22","cited_by_count":1188},{"id":"1711.03213","title":"CyCADA: Cycle-Consistent Adversarial Domain Adaptation","created":"2017-11-08","cited_by_count":1082},{"id":"1706.05394","title":"A Closer Look at Memorization in Deep Networks","created":"2017-06-16","cited_by_count":942},{"id":"2002.08546","title":"Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation","created":"2020-02-20","cited_by_count":848},{"id":"2002.07394","title":"DivideMix: Learning with Noisy Labels as Semi-supervised Learning","created":"2020-02-18","cited_by_count":692},{"id":"1712.02560","title":"Maximum Classifier Discrepancy for Unsupervised Domain Adaptation","created":"2017-12-07","cited_by_count":683},{"id":"1605.06636","title":"Deep Transfer Learning with Joint Adaptation Networks","created":"2016-05-21","cited_by_count":612},{"id":"1412.3474","title":"Deep Domain Confusion: Maximizing for Domain Invariance","created":"2014-12-10","cited_by_count":600},{"id":"2007.08199","title":"Learning from Noisy Labels with Deep Neural Networks: A Survey","created":"2020-07-16","cited_by_count":582},{"id":"1911.05371","title":"Self-labelling via simultaneous clustering and representation learning","created":"2019-11-13","cited_by_count":528},{"id":"1908.02983","title":"Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning","created":"2019-08-08","cited_by_count":510},{"id":"1710.06924","title":"VisDA: The Visual Domain Adaptation Challenge","created":"2017-10-18","cited_by_count":435},{"id":"1901.00976","title":"Contrastive Adaptation Network for Unsupervised Domain Adaptation","created":"2019-01-04","cited_by_count":407},{"id":"2102.09508","title":"Domain Adaptation for Medical Image Analysis: A Survey","created":"2021-02-18","cited_by_count":352},{"id":"1904.05801","title":"Bridging Theory and Algorithm for Domain Adaptation","created":"2019-04-11","cited_by_count":332},{"id":"1904.11238","title":"Unsupervised Label Noise Modeling and Loss Correction","created":"2019-04-25","cited_by_count":328},{"id":"1802.08735","title":"A DIRT-T Approach to Unsupervised Domain Adaptation","created":"2018-02-23","cited_by_count":215},{"id":"1510.02192","title":"Simultaneous Deep Transfer Across Domains and Tasks","created":"2015-10-08","cited_by_count":206},{"id":"2004.07703","title":"Unsupervised Intra-domain Adaptation for Semantic Segmentation through Self-Supervision","created":"2020-04-16","cited_by_count":198},{"id":"1906.03950","title":"Domain-Specific Batch Normalization for Unsupervised Domain Adaptation","created":"2019-05-27","cited_by_count":197},{"id":"2012.07297","title":"Source Data-absent Unsupervised Domain Adaptation through Hypothesis Transfer and Labeling Transfer","created":"2020-12-14","cited_by_count":173},{"id":"2002.07953","title":"Universal Domain Adaptation through Self Supervision","created":"2020-02-19","cited_by_count":172},{"id":"1702.08400","title":"Asymmetric Tri-training for Unsupervised Domain Adaptation","created":"2017-02-27","cited_by_count":152},{"id":"2110.03374","title":"Model Adaptation: Historical Contrastive Learning for Unsupervised Domain Adaptation without Source Data","created":"2021-10-07","cited_by_count":152},{"id":"1803.05984","title":"Deep Co-Training for Semi-Supervised Image Recognition","created":"2018-03-15","cited_by_count":147},{"id":"2003.12237","title":"Towards Discriminability and Diversity: Batch Nuclear-norm Maximization under Label Insufficient Situations","created":"2020-03-27","cited_by_count":133},{"id":"2003.13183","title":"Gradually Vanishing Bridge for Adversarial Domain Adaptation","created":"2020-03-30","cited_by_count":130},{"id":"2103.03606","title":"Unbalanced minibatch Optimal Transport; applications to Domain Adaptation","created":"2021-03-05","cited_by_count":125},{"id":"2103.03571","title":"Cycle Self-Training for Domain Adaptation","created":"2021-03-05","cited_by_count":120},{"id":"1707.09465","title":"Curriculum Domain Adaptation for Semantic Segmentation of Urban Scenes","created":"2017-07-29","cited_by_count":105},{"id":"2106.04732","title":"AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation","created":"2021-06-08","cited_by_count":104},{"id":"1903.09980","title":"Cluster Alignment with a Teacher for Unsupervised Domain Adaptation","created":"2019-03-24","cited_by_count":88},{"id":"2102.11628","title":"FINE Samples for Learning with Noisy Labels","created":"2021-02-23","cited_by_count":82},{"id":"2007.04171","title":"Domain Adaptation with Auxiliary Target Domain-Oriented Classifier","created":"2020-07-08","cited_by_count":76},{"id":"2006.10032","title":"Self-training Avoids Using Spurious Features Under Domain Shift","created":"2020-06-17","cited_by_count":69},{"id":"1910.05562","title":"Drop to Adapt: Learning Discriminative Features for Unsupervised Domain Adaptation","created":"2019-10-12","cited_by_count":62},{"id":"2007.07222","title":"Collaborative Unsupervised Domain Adaptation for Medical Image Diagnosis","created":"2020-07-05","cited_by_count":60},{"id":"2012.04828","title":"Two-phase Pseudo Label Densification for Self-training based Domain Adaptation","created":"2020-12-09","cited_by_count":60},{"id":"2003.02541","title":"A Balanced and Uncertainty-aware Approach for Partial Domain Adaptation","created":"2020-03-05","cited_by_count":55},{"id":"2104.01539","title":"DINE: Domain Adaptation from Single and Multiple Black-box Predictors","created":"2021-04-04","cited_by_count":51},{"id":"2101.02839","title":"Unsupervised Domain Adaptation of Black-Box Source Models","created":"2021-01-08","cited_by_count":32},{"id":"2107.04932","title":"Aligning Correlation Information for Domain Adaptation in Action Recognition","created":"2021-07-11","cited_by_count":30},{"id":"2203.04559","title":"Source-free Video Domain Adaptation by Learning Temporal Consistency for Action Recognition","created":"2022-03-09","cited_by_count":28},{"id":"2108.00302","title":"Conditional Bures Metric for Domain Adaptation","created":"2021-07-31","cited_by_count":25},{"id":"2001.02950","title":"Generative Pseudo-label Refinement for Unsupervised Domain Adaptation","created":"2020-01-09","cited_by_count":24},{"id":"1908.03884","title":"UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation","created":"2019-08-11","cited_by_count":23},{"id":"2011.14540","title":"Heuristic Domain Adaptation","created":"2020-11-30","cited_by_count":19},{"id":"1611.08195","title":"Domain Adaptation by Mixture of Alignments of Second- or Higher-Order Scatter Tensors","created":"2016-11-24","cited_by_count":18},{"id":"2107.04941","title":"Partial Video Domain Adaptation with Partial Adversarial Temporal Attentive Network","created":"2021-07-11","cited_by_count":17},{"id":"2008.11911","title":"Domain Adaptation Through Task Distillation","created":"2020-08-27","cited_by_count":11},{"id":"2205.00186","title":"Reliable Label Correction is a Good Booster When Learning with Extremely Noisy Labels","created":"2022-04-30","cited_by_count":4}],"next_cursor":null}