{"list":"other","total":69,"rows":[{"id":"1512.03385","title":"Deep Residual Learning for Image Recognition","created":"2015-12-10","cited_by_count":52096},{"id":"1810.04805","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","created":"2018-10-11","cited_by_count":40399},{"id":"2103.00020","title":"Learning Transferable Visual Models From Natural Language Supervision","created":"2021-02-26","cited_by_count":33744},{"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":"1505.07818","title":"Domain-Adversarial Training of Neural Networks","created":"2015-05-28","cited_by_count":3044},{"id":"1812.01754","title":"Moment Matching for Multi-Source Domain Adaptation","created":"2018-12-04","cited_by_count":1331},{"id":"1607.01719","title":"Deep CORAL: Correlation Alignment for Deep Domain Adaptation","created":"2016-07-06","cited_by_count":1200},{"id":"1804.06872","title":"Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels","created":"2018-04-18","cited_by_count":1059},{"id":"1710.03077","title":"Deeper, Broader and Artier Domain Generalization","created":"2017-10-09","cited_by_count":986},{"id":"2103.03097","title":"Generalizing to Unseen Domains: A Survey on Domain Generalization","created":"2021-03-02","cited_by_count":766},{"id":"1710.03463","title":"Learning to Generalize: Meta-Learning for Domain Generalization","created":"2017-10-10","cited_by_count":763},{"id":"2002.07394","title":"DivideMix: Learning with Noisy Labels as Semi-supervised Learning","created":"2020-02-18","cited_by_count":692},{"id":"2104.02008","title":"Domain Generalization with MixStyle","created":"2021-04-05","cited_by_count":566},{"id":"1511.06233","title":"Towards Open Set Deep Networks","created":"2015-11-19","cited_by_count":538},{"id":"2102.08604","title":"SWAD: Domain Generalization by Seeking Flat Minima","created":"2021-02-17","cited_by_count":434},{"id":"1811.08581","title":"Recent Advances in Open Set Recognition: A Survey","created":"2018-11-21","cited_by_count":310},{"id":"2007.03304","title":"Learning to Generate Novel Domains for Domain Generalization","created":"2020-07-07","cited_by_count":296},{"id":"2006.13554","title":"Normalized Loss Functions for Deep Learning with Noisy Labels","created":"2020-06-24","cited_by_count":257},{"id":"2003.06054","title":"Deep Domain-Adversarial Image Generation for Domain Generalisation","created":"2020-03-12","cited_by_count":231},{"id":"1905.05040","title":"Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels","created":"2019-05-13","cited_by_count":209},{"id":"1906.00189","title":"Are Anchor Points Really Indispensable in Label-Noise Learning?","created":"2019-06-01","cited_by_count":208},{"id":"2003.02819","title":"Does label smoothing mitigate label noise?","created":"2020-03-05","cited_by_count":204},{"id":"1907.04275","title":"Learning to Optimize Domain Specific Normalization for Domain Generalization","created":"2019-07-09","cited_by_count":161},{"id":"2003.07325","title":"Domain Adaptive Ensemble Learning","created":"2020-03-16","cited_by_count":156},{"id":"2103.00953","title":"Adversarial Reciprocal Points Learning for Open Set Recognition","created":"2021-03-01","cited_by_count":153},{"id":"1812.04246","title":"Classification-Reconstruction Learning for Open-Set Recognition","created":"2018-12-11","cited_by_count":146},{"id":"1910.03231","title":"Peer Loss Functions: Learning from Noisy Labels without Knowing Noise Rates","created":"2019-10-08","cited_by_count":142},{"id":"2104.02939","title":"OpenGAN: Open-Set Recognition via Open Data Generation","created":"2021-04-07","cited_by_count":135},{"id":"1902.03680","title":"Learning From Noisy Labels By Regularized Estimation Of Annotator Confusion","created":"2019-02-10","cited_by_count":127},{"id":"2009.12829","title":"Domain Generalization for Medical Imaging Classification with Linear-Dependency Regularization","created":"2020-09-27","cited_by_count":123},{"id":"2011.00178","title":"Learning Open Set Network with Discriminative Reciprocal Points","created":"2020-10-31","cited_by_count":115},{"id":"2104.03620","title":"Open Domain Generalization with Domain-Augmented Meta-Learning","created":"2021-04-08","cited_by_count":115},{"id":"2203.04181","title":"Selective-Supervised Contrastive Learning with Noisy Labels","created":"2022-03-08","cited_by_count":112},{"id":"1911.03809","title":"Meta Label Correction for Noisy Label Learning","created":"2019-11-10","cited_by_count":108},{"id":"1708.01682","title":"Deep Metric Learning with Angular Loss","created":"2017-08-04","cited_by_count":107},{"id":"2009.05448","title":"Heterogeneous Domain Generalization via Domain Mixup","created":"2020-09-11","cited_by_count":103},{"id":"2103.16050","title":"Progressive Domain Expansion Network for Single Domain Generalization","created":"2021-03-30","cited_by_count":97},{"id":"2107.10161","title":"Evidential Deep Learning for Open Set Action Recognition","created":"2021-07-21","cited_by_count":96},{"id":"2202.14026","title":"Robust Training under Label Noise by Over-parameterization","created":"2022-02-28","cited_by_count":93},{"id":"2012.11854","title":"A Second-Order Approach to Learning with Instance-Dependent Label Noise","created":"2020-12-22","cited_by_count":90},{"id":"2012.05458","title":"Beyond Class-Conditional Assumption: A Primary Attempt to Combat Instance-Dependent Label Noise","created":"2020-12-10","cited_by_count":82},{"id":"2102.11628","title":"FINE Samples for Learning with Noisy Labels","created":"2021-02-23","cited_by_count":82},{"id":"2203.14542","title":"UNICON: Combating Label Noise Through Uniform Selection and Contrastive Learning","created":"2022-03-28","cited_by_count":77},{"id":"2011.10077","title":"Error-Bounded Correction of Noisy Labels","created":"2020-11-19","cited_by_count":74},{"id":"2106.04149","title":"To Smooth or Not? When Label Smoothing Meets Noisy Labels","created":"2021-06-08","cited_by_count":70},{"id":"2102.05291","title":"Clusterability as an Alternative to Anchor Points When Learning with Noisy Labels","created":"2021-02-10","cited_by_count":65},{"id":"2106.10891","title":"Open-set Label Noise Can Improve Robustness Against Inherent Label Noise","created":"2021-06-21","cited_by_count":59},{"id":"2211.00181","title":"The Numerical Stability of Hyperbolic Representation Learning","created":"2022-10-31","cited_by_count":47},{"id":"1907.12865","title":"Open Set Domain Adaptation for Image and Action Recognition","created":"2019-07-30","cited_by_count":31},{"id":"2008.11586","title":"Weakly Supervised Learning with Side Information for Noisy Labeled Images","created":"2020-08-25","cited_by_count":30},{"id":"2207.02158","title":"Class-Specific Semantic Reconstruction for Open Set Recognition","created":"2022-07-05","cited_by_count":28},{"id":"2303.06930","title":"Twin Contrastive Learning with Noisy Labels","created":"2023-03-13","cited_by_count":28},{"id":"2303.11674","title":"ALOFT: A Lightweight MLP-like Architecture with Dynamic Low-frequency Transform for Domain Generalization","created":"2023-03-21","cited_by_count":28},{"id":"2202.01273","title":"Beyond Images: Label Noise Transition Matrix Estimation for Tasks with Lower-Quality Features","created":"2022-02-02","cited_by_count":27},{"id":"2110.09022","title":"Mitigating Memorization of Noisy Labels via Regularization between Representations","created":"2021-10-18","cited_by_count":26},{"id":"2303.12698","title":"Open Set Action Recognition via Multi-Label Evidential Learning","created":"2023-02-27","cited_by_count":26},{"id":"2308.09391","title":"Generalizable Decision Boundaries: Dualistic Meta-Learning for Open Set Domain Generalization","created":"2023-08-18","cited_by_count":24},{"id":"2312.09505","title":"Adaptive Integration of Partial Label Learning and Negative Learning for Enhanced Noisy Label Learning","created":"2023-12-15","cited_by_count":22},{"id":"2404.00710","title":"Unknown Prompt, the only Lacuna: Unveiling CLIP's Potential for Open Domain Generalization","created":"2024-03-31","cited_by_count":20},{"id":"2207.13316","title":"NICEST: Noisy Label Correction and Training for Robust Scene Graph Generation","created":"2022-07-27","cited_by_count":19},{"id":"2203.15202","title":"SimT: Handling Open-set Noise for Domain Adaptive Semantic Segmentation","created":"2022-03-29","cited_by_count":17},{"id":"2208.08464","title":"CTRL: Clustering Training Losses for Label Error Detection","created":"2022-08-17","cited_by_count":17},{"id":"2312.06330","title":"Navigating Open Set Scenarios for Skeleton-based Action Recognition","created":"2023-12-11","cited_by_count":17},{"id":"2011.00444","title":"Discriminative Adversarial Domain Generalization with Meta-learning based Cross-domain Validation","created":"2020-11-01","cited_by_count":16},{"id":"2305.18377","title":"BadLabel: A Robust Perspective on Evaluating and Enhancing Label-noise Learning","created":"2023-05-28","cited_by_count":16},{"id":"2207.09963","title":"Rethinking Few-Shot Class-Incremental Learning with Open-Set Hypothesis in Hyperbolic Geometry","created":"2022-07-20","cited_by_count":12},{"id":"2409.17555","title":"Advancing Open-Set Domain Generalization Using Evidential Bi-Level Hardest Domain Scheduler","created":"2024-09-26","cited_by_count":12},{"id":"2403.09975","title":"Skeleton-Based Human Action Recognition with Noisy Labels","created":"2024-03-15","cited_by_count":8},{"id":"2405.05714","title":"Estimating Noisy Class Posterior with Part-level Labels for Noisy Label Learning","created":"2024-05-08","cited_by_count":7}],"next_cursor":null}