{"list":"other","total":66,"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":"1409.1556","title":"Very Deep Convolutional Networks for Large-Scale Image Recognition","created":"2014-09-04","cited_by_count":15364},{"id":"2002.05709","title":"A Simple Framework for Contrastive Learning of Visual Representations","created":"2020-02-13","cited_by_count":12049},{"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":"1512.00567","title":"Rethinking the Inception Architecture for Computer Vision","created":"2015-12-02","cited_by_count":5475},{"id":"2006.07733","title":"Bootstrap your own latent: A new approach to self-supervised Learning","created":"2020-06-13","cited_by_count":4750},{"id":"1409.4842","title":"Going Deeper with Convolutions","created":"2014-09-17","cited_by_count":4241},{"id":"2011.10566","title":"Exploring Simple Siamese Representation Learning","created":"2020-11-20","cited_by_count":2866},{"id":"1611.03530","title":"Understanding deep learning requires rethinking generalization","created":"2016-11-10","cited_by_count":1674},{"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":"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":"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":"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":"2007.00151","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":"1412.6596","title":"Training Deep Neural Networks on Noisy Labels with Bootstrapping","created":"2014-12-20","cited_by_count":322},{"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":"2205.07246","title":"FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning","created":"2022-05-15","cited_by_count":232},{"id":"1706.02613","title":"Decoupling \"when to update\" from \"how to update\"","created":"2017-06-08","cited_by_count":230},{"id":"2203.03884","title":"Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels","created":"2022-03-08","cited_by_count":218},{"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":"1711.07131","title":"CleanNet: Transfer Learning for Scalable Image Classifier Training with Label Noise","created":"2017-11-20","cited_by_count":181},{"id":"1806.02612","title":"Dimensionality-Driven Learning with Noisy Labels","created":"2018-06-07","cited_by_count":173},{"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":"2002.10319","title":"Self-Adaptive Training: beyond Empirical Risk Minimization","created":"2020-02-24","cited_by_count":126},{"id":"1908.02160","title":"Deep Self-Learning From Noisy Labels","created":"2019-08-06","cited_by_count":122},{"id":"2002.08053","title":"Progressive Identification of True Labels for Partial-Label Learning","created":"2020-02-19","cited_by_count":102},{"id":"2103.07756","title":"Learning with Feature-Dependent Label Noise: A Progressive Approach","created":"2021-03-13","cited_by_count":102},{"id":"2102.02400","title":"Provably End-to-end Label-Noise Learning without Anchor Points","created":"2021-02-04","cited_by_count":97},{"id":"2103.14581","title":"Non-Salient Region Object Mining for Weakly Supervised Semantic Segmentation","created":"2021-03-26","cited_by_count":95},{"id":"2202.14026","title":"Robust Training under Label Noise by Over-parameterization","created":"2022-02-28","cited_by_count":93},{"id":"1705.07541","title":"Learning from Complementary Labels","created":"2017-05-22","cited_by_count":88},{"id":"2102.11628","title":"FINE Samples for Learning with Noisy Labels","created":"2021-02-23","cited_by_count":82},{"id":"2103.13029","title":"Jo-SRC: A Contrastive Approach for Combating Noisy Labels","created":"2021-03-24","cited_by_count":80},{"id":"2012.04835","title":"A Topological Filter for Learning with Label Noise","created":"2020-12-09","cited_by_count":76},{"id":"2202.02200","title":"Learning with Neighbor Consistency for Noisy Labels","created":"2022-02-04","cited_by_count":55},{"id":"2403.06258","title":"Poly Kernel Inception Network for Remote Sensing Detection","created":"2024-03-10","cited_by_count":52},{"id":"2110.12911","title":"Instance-Dependent Partial Label Learning","created":"2021-10-25","cited_by_count":49},{"id":"2108.02399","title":"Webly Supervised Fine-Grained Recognition: Benchmark Datasets and An Approach","created":"2021-08-05","cited_by_count":32},{"id":"2105.13001","title":"Estimating Instance-dependent Bayes-label Transition Matrix using a Deep Neural Network","created":"2021-05-27","cited_by_count":30},{"id":"2202.05613","title":"CMW-Net: Learning a Class-Aware Sample Weighting Mapping for Robust Deep Learning","created":"2022-02-11","cited_by_count":24},{"id":"1901.03073","title":"GM-PLL: Graph Matching based Partial Label Learning","created":"2019-01-10","cited_by_count":23},{"id":"2208.03207","title":"Neighborhood Collective Estimation for Noisy Label Identification and Correction","created":"2022-08-05","cited_by_count":22},{"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":"2204.02620","title":"Towards Robust Adaptive Object Detection under Noisy Annotations","created":"2022-04-06","cited_by_count":20},{"id":"2402.19082","title":"VideoMAC: Video Masked Autoencoders Meet ConvNets","created":"2024-02-29","cited_by_count":20},{"id":"2008.02427","title":"Salvage Reusable Samples from Noisy Data for Robust Learning","created":"2020-08-06","cited_by_count":19},{"id":"2010.15603","title":"Suppressing Mislabeled Data via Grouping and Self-Attention","created":"2020-10-29","cited_by_count":17},{"id":"2504.02901","title":"Hide and Seek in Noise Labels: Noise-Robust Collaborative Active Learning with LLM-Powered Assistance","created":"2025-04-03","cited_by_count":11},{"id":"2105.03059","title":"Self-paced Resistance Learning against Overfitting on Noisy Labels","created":"2021-05-07","cited_by_count":6},{"id":"2411.17113","title":"Learning from Noisy Labels via Conditional Distributionally Robust Optimization","created":"2024-11-26","cited_by_count":6},{"id":"2211.11753","title":"SplitNet: Learnable Clean-Noisy Label Splitting for Learning with Noisy Labels","created":"2022-11-20","cited_by_count":5},{"id":"2305.19486","title":"Instance-dependent Noisy-label Learning with Graphical Model Based Noise-rate Estimation","created":"2023-05-31","cited_by_count":4},{"id":"2002.08043","title":"Meta Segmentation Network for Ultra-Resolution Medical Images","created":"2020-02-19","cited_by_count":3},{"id":"2502.19816","title":"Twofold Debiasing Enhances Fine-Grained Learning with Coarse Labels","created":"2025-02-27","cited_by_count":1}],"next_cursor":null}