{"list":"other","total":57,"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":"2002.05709","title":"A Simple Framework for Contrastive Learning of Visual Representations","created":"2020-02-13","cited_by_count":12049},{"id":"1710.09412","title":"mixup: Beyond Empirical Risk Minimization","created":"2017-10-25","cited_by_count":5183},{"id":"1506.02640","title":"You Only Look Once: Unified, Real-Time Object Detection","created":"2015-06-08","cited_by_count":4852},{"id":"2004.11362","title":"Supervised Contrastive Learning","created":"2020-04-23","cited_by_count":2829},{"id":"2001.07685","title":"FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence","created":"2020-01-21","cited_by_count":2085},{"id":"1602.07261","title":"Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning","created":"2016-02-23","cited_by_count":1512},{"id":"1905.02249","title":"MixMatch: A Holistic Approach to Semi-Supervised Learning","created":"2019-05-06","cited_by_count":1391},{"id":"1911.04252","title":"Self-training with Noisy Student improves ImageNet classification","created":"2019-11-11","cited_by_count":1311},{"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":"1805.09501","title":"AutoAugment: Learning Augmentation Policies from Data","created":"2018-05-24","cited_by_count":730},{"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":"1703.01780","title":"Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results","created":"2017-03-06","cited_by_count":472},{"id":"2007.00151","title":"Early-Learning Regularization Prevents Memorization of Noisy Labels","created":"2020-06-30","cited_by_count":416},{"id":"1901.04215","title":"How does Disagreement Help Generalization against Label Corruption?","created":"2019-01-14","cited_by_count":390},{"id":"1505.04366","title":"Learning Deconvolution Network for Semantic Segmentation","created":"2015-05-17","cited_by_count":365},{"id":"2101.11605","title":"Bottleneck Transformers for Visual Recognition","created":"2021-01-27","cited_by_count":364},{"id":"1904.11238","title":"Unsupervised Label Noise Modeling and Loss Correction","created":"2019-04-25","cited_by_count":328},{"id":"2101.06329","title":"In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning","created":"2021-01-15","cited_by_count":301},{"id":"2103.02603","title":"Towards Open World Object Detection","created":"2021-03-03","cited_by_count":297},{"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":"1706.02613","title":"Decoupling \"when to update\" from \"how to update\"","created":"2017-06-08","cited_by_count":230},{"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":"1806.02612","title":"Dimensionality-Driven Learning with Noisy Labels","created":"2018-06-07","cited_by_count":173},{"id":"1812.05214","title":"Learning to Learn from Noisy Labeled Data","created":"2018-12-13","cited_by_count":160},{"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":"1910.03231","title":"Peer Loss Functions: Learning from Noisy Labels without Knowing Noise Rates","created":"2019-10-08","cited_by_count":142},{"id":"2103.14267","title":"Contrastive Learning based Hybrid Networks for Long-Tailed Image Classification","created":"2021-03-26","cited_by_count":130},{"id":"1804.00092","title":"Iterative Learning with Open-set Noisy Labels","created":"2018-03-31","cited_by_count":108},{"id":"1911.03809","title":"Meta Label Correction for Noisy Label Learning","created":"2019-11-10","cited_by_count":108},{"id":"2103.07756","title":"Learning with Feature-Dependent Label Noise: A Progressive Approach","created":"2021-03-13","cited_by_count":102},{"id":"2103.14581","title":"Non-Salient Region Object Mining for Weakly Supervised Semantic Segmentation","created":"2021-03-26","cited_by_count":95},{"id":"2012.04462","title":"Multi-Objective Interpolation Training for Robustness to Label Noise","created":"2020-12-08","cited_by_count":86},{"id":"2103.02130","title":"Augmentation Strategies for Learning with Noisy Labels","created":"2021-03-03","cited_by_count":86},{"id":"2103.13029","title":"Jo-SRC: A Contrastive Approach for Combating Noisy Labels","created":"2021-03-24","cited_by_count":80},{"id":"1705.01936","title":"Learning with Confident Examples: Rank Pruning for Robust Classification with Noisy Labels","created":"2017-05-04","cited_by_count":52},{"id":"2104.00905","title":"Background-Aware Pooling and Noise-Aware Loss for Weakly-Supervised Semantic Segmentation","created":"2021-04-02","cited_by_count":41},{"id":"1912.05086","title":"Learning from Noisy Anchors for One-stage Object Detection","created":"2019-12-11","cited_by_count":35},{"id":"2104.06574","title":"Joint Negative and Positive Learning for Noisy Labels","created":"2021-04-14","cited_by_count":34},{"id":"1901.07759","title":"Robust Learning at Noisy Labeled Medical Images: Applied to Skin Lesion Classification","created":"2019-01-23","cited_by_count":33},{"id":"2009.08891","title":"AdderSR: Towards Energy Efficient Image Super-Resolution","created":"2020-09-18","cited_by_count":32},{"id":"1802.02679","title":"A Semi-Supervised Two-Stage Approach to Learning from Noisy Labels","created":"2018-02-08","cited_by_count":29},{"id":"2103.04618","title":"Joint Noise-Tolerant Learning and Meta Camera Shift Adaptation for Unsupervised Person Re-Identification","created":"2021-03-08","cited_by_count":29},{"id":"1909.03388","title":"L_DMI: An Information-theoretic Noise-robust Loss Function","created":"2019-09-08","cited_by_count":26},{"id":"2103.16047","title":"Noise-resistant Deep Metric Learning with Ranking-based Instance Selection","created":"2021-03-30","cited_by_count":23},{"id":"1903.10357","title":"Noise-Tolerant Paradigm for Training Face Recognition CNNs","created":"2019-03-25","cited_by_count":20},{"id":"2003.06729","title":"NoiseRank: Unsupervised Label Noise Reduction with Dependence Models","created":"2020-03-15","cited_by_count":20},{"id":"2009.08325","title":"Noisy Concurrent Training for Efficient Learning under Label Noise","created":"2020-09-17","cited_by_count":13},{"id":"2104.00246","title":"Divergence Optimization for Noisy Universal Domain Adaptation","created":"2021-04-01","cited_by_count":13}],"next_cursor":null}