Learning to Reweight Examples for Robust Deep Learning

24 March 2018

Papers citing "Learning to Reweight Examples for Robust Deep Learning"

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Title
Towards a Robust Differentiable Architecture Search under Label Noise Christian Simon Piotr Koniusz L. Petersson Yan Han Mehrtash Harandi NoLa AAML OOD 17 4 0 23 Oct 2021
PropMix: Hard Sample Filtering and Proportional MixUp for Learning with Noisy Labels F. Cordeiro Vasileios Belagiannis Ian Reid G. Carneiro NoLa 30 18 0 22 Oct 2021
Prototypical Classifier for Robust Class-Imbalanced Learning Tong Wei Jiang-Xin Shi Yu-Feng Li Min-Ling Zhang NoLa 9 16 0 22 Oct 2021
ABC: Auxiliary Balanced Classifier for Class-imbalanced Semi-supervised Learning Hyuck Lee Seungjae Shin Heeyoung Kim CLL 24 90 0 20 Oct 2021
Tackling the Imbalance for GNNs Rui Wang Weixuan Xiong Qing-Hu Hou Ou Wu 52 6 0 17 Oct 2021
Continual Learning on Noisy Data Streams via Self-Purified Replay C. Kim Jinseo Jeong Sang-chul Moon Gunhee Kim CLL 40 39 0 14 Oct 2021
Adversarial Attack across Datasets Yunxiao Qin Yuanhao Xiong Jinfeng Yi Lihong Cao Cho-Jui Hsieh AAML 35 3 0 13 Oct 2021
LiST: Lite Prompted Self-training Makes Parameter-Efficient Few-shot Learners Yaqing Wang Subhabrata Mukherjee Xiaodong Liu Jing Gao Ahmed Hassan Awadallah Jianfeng Gao VLM BDL 51 10 0 12 Oct 2021
Self-supervised Learning is More Robust to Dataset Imbalance Hong Liu Jeff Z. HaoChen Adrien Gaidon Tengyu Ma OOD SSL 33 157 0 11 Oct 2021
Which Samples Should be Learned First: Easy or Hard? Xiaoling Zhou Ou Wu 26 17 0 11 Oct 2021
Class-Balanced Active Learning for Image Classification Javad Zolfaghari Bengar Joost van de Weijer Laura Lopez-Fuentes Bogdan Raducanu 11 22 0 09 Oct 2021
Observations on K-image Expansion of Image-Mixing Augmentation for Classification Joonhyun Jeong Sungmin Cha Jongwon Choi Sangdoo Yun Taesup Moon Y. Yoo VLM 21 6 0 08 Oct 2021
Topology-Imbalance Learning for Semi-Supervised Node Classification Deli Chen Yankai Lin Guangxiang Zhao Xuancheng Ren Peng Li Jie Zhou Xu Sun 21 87 0 08 Oct 2021
Online Hyperparameter Meta-Learning with Hypergradient Distillation Haebeom Lee Hayeon Lee Jaewoong Shin Eunho Yang Timothy M. Hospedales Sung Ju Hwang DD 30 2 0 06 Oct 2021
MetaPix: Domain Transfer for Semantic Segmentation by Meta Pixel Weighting Yiren Jian Chongyang Gao 36 4 0 05 Oct 2021
An Empirical Investigation of Learning from Biased Toxicity Labels Neel Nanda J. Uesato Sven Gowal 20 0 0 04 Oct 2021
Adversarial Regression with Doubly Non-negative Weighting Matrices Tam Le Truyen V. Nguyen M. Yamada Jose H. Blanchet Viet Anh Nguyen 24 5 0 30 Sep 2021
Active Refinement for Multi-Label Learning: A Pseudo-Label Approach Cheng-Yu Hsieh Weiliang Lin Miao Xu Gang Niu Hsuan-Tien Lin Masashi Sugiyama LRM 21 1 0 29 Sep 2021
Robust Temporal Ensembling for Learning with Noisy Labels Abel Brown Benedikt Schifferer R. DiPietro NoLa OOD 6 0 0 29 Sep 2021
Learning to Selectively Learn for Weakly-supervised Paraphrase Generation Kaize Ding Dingcheng Li A. Li Xing Fan Chenlei Guo Yang Liu Huan Liu 34 6 0 25 Sep 2021
Learning to Robustly Aggregate Labeling Functions for Semi-supervised Data Programming Ayush Maheshwari Krishnateja Killamsetty Ganesh Ramakrishnan Rishabh K. Iyer Marina Danilevsky Lucian Popa 38 16 0 23 Sep 2021
Label Cleaning Multiple Instance Learning: Refining Coarse Annotations on Single Whole-Slide Images Zhenzhen Wang Carla Saoud A. Popel Aaron W. James Aleksander S. Popel Jeremias Sulam 21 21 0 22 Sep 2021
The Trade-offs of Domain Adaptation for Neural Language Models David Grangier Dan Iter 29 21 0 21 Sep 2021
Knowledge Distillation with Noisy Labels for Natural Language Understanding Shivendra Bhardwaj Abbas Ghaddar Ahmad Rashid Khalil Bibi Cheng-huan Li A. Ghodsi Philippe Langlais Mehdi Rezagholizadeh 19 1 0 21 Sep 2021
Self-training with Few-shot Rationalization: Teacher Explanations Aid Student in Few-shot NLU Meghana Moorthy Bhat Alessandro Sordoni Subhabrata Mukherjee LRM 17 24 0 17 Sep 2021
Multihop: Leveraging Complex Models to Learn Accurate Simple Models Amit Dhurandhar Tejaswini Pedapati 19 0 0 14 Sep 2021
Not All Negatives are Equal: Label-Aware Contrastive Loss for Fine-grained Text Classification Varsha Suresh Desmond C. Ong VLM 69 79 0 12 Sep 2021
Co-Correcting: Noise-tolerant Medical Image Classification via mutual Label Correction Jiarun Liu Ruirui Li Chuan Sun OOD NoLa VLM 27 32 0 11 Sep 2021
Assessing the Quality of the Datasets by Identifying Mislabeled Samples Vaibhav Pulastya Gaurav Nuti Yash Kumar Atri Tanmoy Chakraborty NoLa 35 5 0 10 Sep 2021
MetaXT: Meta Cross-Task Transfer between Disparate Label Spaces Srinagesh Sharma Guoqing Zheng Ahmed Hassan Awadallah 27 1 0 09 Sep 2021
Learning Fast Sample Re-weighting Without Reward Data Zizhao Zhang Tomas Pfister 28 74 0 07 Sep 2021
On-target Adaptation Dequan Wang Shaoteng Liu Sayna Ebrahimi Evan Shelhamer Trevor Darrell TTA 34 19 0 02 Sep 2021
Text AutoAugment: Learning Compositional Augmentation Policy for Text Classification Shuhuai Ren Jinchao Zhang Lei Li Xu Sun Jie Zhou 38 31 0 01 Sep 2021
Calibrating Class Activation Maps for Long-Tailed Visual Recognition Chi Zhang Guosheng Lin Lvlong Lai Henghui Ding Qingyao Wu 29 1 0 29 Aug 2021
WALNUT: A Benchmark on Semi-weakly Supervised Learning for Natural Language Understanding Guoqing Zheng Giannis Karamanolakis Kai Shu Ahmed Hassan Awadallah SSL 21 1 0 28 Aug 2021
NGC: A Unified Framework for Learning with Open-World Noisy Data Zhi-Fan Wu Tong Wei Jianwen Jiang Chaojie Mao Mingqian Tang Yu-Feng Li 11 80 0 25 Aug 2021
Contrastive Representations for Label Noise Require Fine-Tuning Pierre Nodet V. Lemaire A. Bondu Antoine Cornuéjols 23 1 0 20 Aug 2021
Confidence Adaptive Regularization for Deep Learning with Noisy Labels Yangdi Lu Yang Bo Wenbo He NoLa 30 10 0 18 Aug 2021
IPOF: An Extremely and Excitingly Simple Outlier Detection Booster via Infinite Propagation Sibo Zhu Han Zhao Hongfu Liu 16 0 0 01 Aug 2021
Parametric Contrastive Learning Jiequan Cui Zhisheng Zhong Shu Liu Bei Yu Jiaya Jia 30 269 0 26 Jul 2021
Compensation Learning Rujing Yao Ou Wu 16 2 0 26 Jul 2021
An Instance-Dependent Simulation Framework for Learning with Label Noise Keren Gu Xander Masotto Vandana Bachani Balaji Lakshminarayanan Jack Nikodem Dong Yin NoLa 16 24 0 23 Jul 2021
Bias Loss for Mobile Neural Networks L. Abrahamyan Valentin Ziatchin Yiming Chen Nikos Deligiannis 15 14 0 23 Jul 2021
Superpixel-guided Iterative Learning from Noisy Labels for Medical Image Segmentation Shuailin Li Zhitong Gao Xuming He NoLa 27 26 0 21 Jul 2021
Just Train Twice: Improving Group Robustness without Training Group Information E. Liu Behzad Haghgoo Annie S. Chen Aditi Raghunathan Pang Wei Koh Shiori Sagawa Percy Liang Chelsea Finn OOD 37 538 0 19 Jul 2021
Temporal-aware Language Representation Learning From Crowdsourced Labels Y. Hao X. Zhai Wenbiao Ding Zitao Liu AI4TS 18 1 0 15 Jul 2021
Consensual Collaborative Training And Knowledge Distillation Based Facial Expression Recognition Under Noisy Annotations Darshan Gera B. S 13 7 0 10 Jul 2021
Mitigating Memorization in Sample Selection for Learning with Noisy Labels Kyeongbo Kong Junggi Lee Youngchul Kwak Young-Rae Cho Seong-Eun Kim Woo‐Jin Song NoLa 18 0 0 08 Jul 2021
On Bridging Generic and Personalized Federated Learning for Image Classification Hong-You Chen Wei-Lun Chao FedML 22 21 0 02 Jul 2021
Few-Shot Learning with a Strong Teacher Han-Jia Ye Lu Ming De-Chuan Zhan Wei-Lun Chao 19 50 0 01 Jul 2021