{"list":"other","total":54,"rows":[{"id":"1412.6980","title":"Adam: A Method for Stochastic Optimization","created":"2014-12-22","cited_by_count":37240},{"id":"1708.07747","title":"Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms","created":"2017-08-25","cited_by_count":4349},{"id":"1611.03530","title":"Understanding deep learning requires rethinking generalization","created":"2016-11-10","cited_by_count":1674},{"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":"1805.07836","title":"Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels","created":"2018-05-20","cited_by_count":997},{"id":"1706.05394","title":"A Closer Look at Memorization in Deep Networks","created":"2017-06-16","cited_by_count":942},{"id":"1910.09217","title":"Decoupling Representation and Classifier for Long-Tailed Recognition","created":"2019-10-21","cited_by_count":809},{"id":"2002.07394","title":"DivideMix: Learning with Noisy Labels as Semi-supervised Learning","created":"2020-02-18","cited_by_count":692},{"id":"1609.03683","title":"Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach","created":"2016-09-13","cited_by_count":665},{"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":"2007.08199","title":"Learning from Noisy Labels with Deep Neural Networks: A Survey","created":"2020-07-16","cited_by_count":582},{"id":"1505.00853","title":"Empirical Evaluation of Rectified Activations in Convolutional Network","created":"2015-05-05","cited_by_count":519},{"id":"2007.00151","title":"Early-Learning Regularization Prevents Memorization of Noisy Labels","created":"2020-06-30","cited_by_count":416},{"id":"1904.11238","title":"Unsupervised Label Noise Modeling and Loss Correction","created":"2019-04-25","cited_by_count":328},{"id":"1803.11364","title":"Joint Optimization Framework for Learning with Noisy Labels","created":"2018-03-30","cited_by_count":293},{"id":"2003.02752","title":"Combating noisy labels by agreement: A joint training method with co-regularization","created":"2020-03-05","cited_by_count":289},{"id":"1708.02862","title":"WebVision Database: Visual Learning and Understanding from Web Data","created":"2017-08-09","cited_by_count":262},{"id":"2001.10528","title":"Identifying Mislabeled Data using the Area Under the Margin Ranking","created":"2020-01-28","cited_by_count":261},{"id":"2006.13554","title":"Normalized Loss Functions for Deep Learning with Noisy Labels","created":"2020-06-24","cited_by_count":257},{"id":"1706.02613","title":"Decoupling \"when to update\" from \"how to update\"","created":"2017-06-08","cited_by_count":230},{"id":"1901.01588","title":"PyOD: A Python Toolbox for Scalable Outlier Detection","created":"2019-01-06","cited_by_count":222},{"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":"1903.07788","title":"Probabilistic End-to-end Noise Correction for Learning with Noisy Labels","created":"2019-03-19","cited_by_count":210},{"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":"1806.02612","title":"Dimensionality-Driven Learning with Noisy Labels","created":"2018-06-07","cited_by_count":173},{"id":"2101.06395","title":"Free Lunch for Few-shot Learning: Distribution Calibration","created":"2021-01-16","cited_by_count":165},{"id":"1910.01842","title":"SELF: Learning to Filter Noisy Labels with Self-Ensembling","created":"2019-10-04","cited_by_count":164},{"id":"1903.11680","title":"Gradient Descent with Early Stopping is Provably Robust to Label Noise for Overparameterized Neural Networks","created":"2019-03-27","cited_by_count":159},{"id":"1908.07387","title":"NLNL: Negative Learning for Noisy Labels","created":"2019-08-19","cited_by_count":151},{"id":"1704.07433","title":"Active Bias: Training More Accurate Neural Networks by Emphasizing High Variance Samples","created":"2017-04-24","cited_by_count":147},{"id":"2006.07805","title":"Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning","created":"2020-06-14","cited_by_count":142},{"id":"1804.00092","title":"Iterative Learning with Open-set Noisy Labels","created":"2018-03-31","cited_by_count":108},{"id":"1911.05911","title":"Recent Advances in Algorithmic High-Dimensional Robust Statistics","created":"2019-11-14","cited_by_count":101},{"id":"2102.02400","title":"Provably End-to-end Label-Noise Learning without Anchor Points","created":"2021-02-04","cited_by_count":97},{"id":"1805.08193","title":"Masking: A New Perspective of Noisy Supervision","created":"2018-05-21","cited_by_count":93},{"id":"1905.10045","title":"Curriculum Loss: Robust Learning and Generalization against Label Corruption","created":"2019-05-24","cited_by_count":88},{"id":"2103.02130","title":"Augmentation Strategies for Learning with Noisy Labels","created":"2021-03-03","cited_by_count":86},{"id":"2012.04835","title":"A Topological Filter for Learning with Label Noise","created":"2020-12-09","cited_by_count":76},{"id":"2011.10077","title":"Error-Bounded Correction of Noisy Labels","created":"2020-11-19","cited_by_count":74},{"id":"1901.11300","title":"Robust Inference via Generative Classifiers for Handling Noisy Labels","created":"2019-01-31","cited_by_count":68},{"id":"2102.02414","title":"Learning Noise Transition Matrix from Only Noisy Labels via Total Variation Regularization","created":"2021-02-04","cited_by_count":62},{"id":"1709.03768","title":"Learning with Bounded Instance- and Label-dependent Label Noise","created":"2017-09-12","cited_by_count":57},{"id":"2007.15618","title":"Outlier Robust Mean Estimation with Subgaussian Rates via Stability","created":"2020-07-30","cited_by_count":50},{"id":"2002.07933","title":"Improving Generalization by Controlling Label-Noise Information in Neural Network Weights","created":"2020-02-19","cited_by_count":35},{"id":"1811.03205","title":"Robustness of Conditional GANs to Noisy Labels","created":"2018-11-08","cited_by_count":27},{"id":"2006.07831","title":"Class2Simi: A Noise Reduction Perspective on Learning with Noisy Labels","created":"2020-06-14","cited_by_count":25},{"id":"2011.07451","title":"Coresets for Robust Training of Neural Networks against Noisy Labels","created":"2020-11-15","cited_by_count":24},{"id":"2006.05697","title":"Meta Transition Adaptation for Robust Deep Learning with Noisy Labels","created":"2020-06-10","cited_by_count":17},{"id":"2002.03673","title":"Rethinking Class-Prior Estimation for Positive-Unlabeled Learning","created":"2020-02-10","cited_by_count":15},{"id":"1910.05625","title":"What You See May Not Be What You Get: UCB Bandit Algorithms Robust to {\\epsilon}-Contamination","created":"2019-10-12","cited_by_count":6},{"id":"2103.09468","title":"Learning with Group Noise","created":"2021-03-17","cited_by_count":5}],"next_cursor":null}