{"list":"other","total":60,"rows":[{"id":"1512.03385","title":"Deep Residual Learning for Image Recognition","created":"2015-12-10","cited_by_count":52096},{"id":"1405.0312","title":"Microsoft COCO: Common Objects in Context","created":"2014-05-01","cited_by_count":17608},{"id":"1409.0575","title":"ImageNet Large Scale Visual Recognition Challenge","created":"2014-09-01","cited_by_count":8498},{"id":"1911.05722","title":"Momentum Contrast for Unsupervised Visual Representation Learning","created":"2019-11-13","cited_by_count":7513},{"id":"1411.7766","title":"Deep Learning Face Attributes in the Wild","created":"2014-11-28","cited_by_count":3693},{"id":"1901.05555","title":"Class-Balanced Loss Based on Effective Number of Samples","created":"2019-01-16","cited_by_count":1245},{"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":"1807.05520","title":"Deep Clustering for Unsupervised Learning of Visual Features","created":"2018-07-15","cited_by_count":1028},{"id":"1805.07836","title":"Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels","created":"2018-05-20","cited_by_count":997},{"id":"1906.07413","title":"Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss","created":"2019-06-18","cited_by_count":989},{"id":"1910.09217","title":"Decoupling Representation and Classifier for Long-Tailed Recognition","created":"2019-10-21","cited_by_count":809},{"id":"1803.00567","title":"Computational Optimal Transport","created":"2018-03-01","cited_by_count":780},{"id":"1904.05160","title":"Large-Scale Long-Tailed Recognition in an Open World","created":"2019-04-10","cited_by_count":705},{"id":"2002.07394","title":"DivideMix: Learning with Noisy Labels as Semi-supervised Learning","created":"2020-02-18","cited_by_count":692},{"id":"1803.09050","title":"Learning to Reweight Examples for Robust Deep Learning","created":"2018-03-24","cited_by_count":675},{"id":"1712.05055","title":"MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels","created":"2017-12-14","cited_by_count":653},{"id":"2007.07314","title":"Long-tail learning via logit adjustment","created":"2020-07-14","cited_by_count":618},{"id":"1911.05371","title":"Self-labelling via simultaneous clustering and representation learning","created":"2019-11-13","cited_by_count":528},{"id":"1712.09482","title":"Robust Loss Functions under Label Noise for Deep Neural Networks","created":"2017-12-27","cited_by_count":443},{"id":"2007.00151","title":"Early-Learning Regularization Prevents Memorization of Noisy Labels","created":"2020-06-30","cited_by_count":416},{"id":"1912.02413","title":"BBN: Bilateral-Branch Network with Cumulative Learning for Long-Tailed Visual Recognition","created":"2019-12-05","cited_by_count":403},{"id":"2007.10740","title":"Balanced Meta-Softmax for Long-Tailed Visual Recognition","created":"2020-07-21","cited_by_count":400},{"id":"1803.11364","title":"Joint Optimization Framework for Learning with Noisy Labels","created":"2018-03-30","cited_by_count":293},{"id":"1904.03626","title":"On The Power of Curriculum Learning in Training Deep Networks","created":"2019-04-07","cited_by_count":286},{"id":"2010.01809","title":"Long-tailed Recognition by Routing Diverse Distribution-Aware Experts","created":"2020-10-05","cited_by_count":271},{"id":"2101.10979","title":"Prototypical Pseudo Label Denoising and Target Structure Learning for Domain Adaptive Semantic Segmentation","created":"2021-01-26","cited_by_count":268},{"id":"1708.02862","title":"WebVision Database: Visual Learning and Understanding from Web Data","created":"2017-08-09","cited_by_count":262},{"id":"1802.05300","title":"Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise","created":"2018-02-14","cited_by_count":216},{"id":"2104.00466","title":"Improving Calibration for Long-Tailed Recognition","created":"2021-04-01","cited_by_count":212},{"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":"1711.07131","title":"CleanNet: Transfer Learning for Scalable Image Classifier Training with Label Noise","created":"2017-11-20","cited_by_count":181},{"id":"1703.02391","title":"Learning from Noisy Labels with Distillation","created":"2017-03-07","cited_by_count":161},{"id":"2207.09052","title":"Balanced Contrastive Learning for Long-Tailed Visual Recognition","created":"2022-07-19","cited_by_count":144},{"id":"1908.02160","title":"Deep Self-Learning From Noisy Labels","created":"2019-08-06","cited_by_count":122},{"id":"2203.04181","title":"Selective-Supervised Contrastive Learning with Noisy Labels","created":"2022-03-08","cited_by_count":112},{"id":"2103.07756","title":"Learning with Feature-Dependent Label Noise: A Progressive Approach","created":"2021-03-13","cited_by_count":102},{"id":"2103.12579","title":"MetaSAug: Meta Semantic Augmentation for Long-Tailed Visual Recognition","created":"2021-03-23","cited_by_count":99},{"id":"1901.06783","title":"Dynamic Curriculum Learning for Imbalanced Data Classification","created":"2019-01-21","cited_by_count":93},{"id":"2110.02444","title":"Influence-Balanced Loss for Imbalanced Visual Classification","created":"2021-10-06","cited_by_count":91},{"id":"2203.14542","title":"UNICON: Combating Label Noise Through Uniform Selection and Contrastive Learning","created":"2022-03-28","cited_by_count":77},{"id":"2205.13775","title":"A Survey on Long-Tailed Visual Recognition","created":"2022-05-27","cited_by_count":75},{"id":"2011.10077","title":"Error-Bounded Correction of Noisy Labels","created":"2020-11-19","cited_by_count":74},{"id":"2103.13646","title":"Contrast to Divide: Self-Supervised Pre-Training for Learning with Noisy Labels","created":"2021-03-25","cited_by_count":69},{"id":"1901.11300","title":"Robust Inference via Generative Classifiers for Handling Noisy Labels","created":"2019-01-31","cited_by_count":68},{"id":"2009.07995","title":"MoPro: Webly Supervised Learning with Momentum Prototypes","created":"2020-09-17","cited_by_count":66},{"id":"2108.11035","title":"NGC: A Unified Framework for Learning with Open-World Noisy Data","created":"2021-08-25","cited_by_count":58},{"id":"2210.17067","title":"Unified Optimal Transport Framework for Universal Domain Adaptation","created":"2022-10-31","cited_by_count":57},{"id":"2006.15766","title":"Heteroskedastic and Imbalanced Deep Learning with Adaptive Regularization","created":"2020-06-29","cited_by_count":49},{"id":"2206.02791","title":"Instance-Dependent Label-Noise Learning with Manifold-Regularized Transition Matrix Estimation","created":"2022-06-06","cited_by_count":49},{"id":"2203.09744","title":"Class-Balanced Pixel-Level Self-Labeling for Domain Adaptive Semantic Segmentation","created":"2022-03-18","cited_by_count":47},{"id":"1910.12795","title":"Learning Data Manipulation for Augmentation and Weighting","created":"2019-10-28","cited_by_count":45},{"id":"2108.11569","title":"Robust Long-Tailed Learning under Label Noise","created":"2021-08-26","cited_by_count":36},{"id":"2207.05471","title":"Uncertainty-Aware Learning Against Label Noise on Imbalanced Datasets","created":"2022-07-12","cited_by_count":28},{"id":"2306.15925","title":"Subclass-balancing Contrastive Learning for Long-tailed Recognition","created":"2023-06-28","cited_by_count":26},{"id":"2208.02951","title":"Learning to Re-weight Examples with Optimal Transport for Imbalanced Classification","created":"2022-08-05","cited_by_count":23},{"id":"2211.10955","title":"When Noisy Labels Meet Long Tail Dilemmas: A Representation Calibration Method","created":"2022-11-20","cited_by_count":21},{"id":"2207.13378","title":"Identifying Hard Noise in Long-Tailed Sample Distribution","created":"2022-07-27","cited_by_count":19},{"id":"2208.09833","title":"Label-Noise Learning with Intrinsically Long-Tailed Data","created":"2022-08-21","cited_by_count":17},{"id":"1911.02377","title":"Searching to Exploit Memorization Effect in Learning from Corrupted Labels","created":"2019-11-06","cited_by_count":11}],"next_cursor":null}