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
LIMIS: Locally Interpretable Modeling using Instance-wise Subsampling Jinsung Yoon Sercan Ö. Arik Tomas Pfister FAtt 10 2 0 26 Sep 2019
Data Valuation using Reinforcement Learning Jinsung Yoon Sercan Ö. Arik Tomas Pfister TDI 25 173 0 25 Sep 2019
Anchor Loss: Modulating Loss Scale based on Prediction Difficulty Serim Ryou Seong-Gyun Jeong Pietro Perona 13 43 0 24 Sep 2019
A Simple yet Effective Baseline for Robust Deep Learning with Noisy Labels Yucen Luo Jun Zhu Tomas Pfister NoLa 10 6 0 20 Sep 2019
Using Latent Codes for Class Imbalance Problem in Unsupervised Domain Adaptation Boris Chidlovskii 11 2 0 17 Sep 2019
Unsupervised Domain Adaptation for Depth Prediction from Images A. Tonioni Matteo Poggi S. Mattoccia Luigi Di Stefano MDE 19 82 0 09 Sep 2019
L_DMI: An Information-theoretic Noise-robust Loss Function Yilun Xu Peng Cao Yuqing Kong Yizhou Wang NoLa 8 57 0 08 Sep 2019
Learning with Noisy Labels for Sentence-level Sentiment Classification Hao Wang Bing-Quan Liu Chaozhuo Li Yan Yang Tianrui Li NoLa 13 26 0 31 Aug 2019
Learning to Transfer Learn: Reinforcement Learning-Based Selection for Adaptive Transfer Learning Linchao Zhu Sercan Ö. Arik Yezhou Yang Tomas Pfister 19 5 0 29 Aug 2019
MetaMixUp: Learning Adaptive Interpolation Policy of MixUp with Meta-Learning Zhijun Mai Guosheng Hu Dexiong Chen Fumin Shen Heng Tao Shen 22 41 0 27 Aug 2019
Mobility-aware Content Preference Learning in Decentralized Caching Networks Yu Ye Ming Xiao Mikael Skoglund 9 18 0 22 Aug 2019
Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement Zhiqiang Shen Zhankui He Wanyun Cui Jiahui Yu Yutong Zheng Chenchen Zhu Marios Savvides AAML 17 5 0 22 Aug 2019
NLNL: Negative Learning for Noisy Labels Youngdong Kim Junho Yim Juseung Yun Junmo Kim NoLa 17 265 0 19 Aug 2019
On Defending Against Label Flipping Attacks on Malware Detection Systems R. Taheri R. Javidan Mohammad Shojafar Zahra Pooranian A. Miri Mauro Conti AAML 19 88 0 13 Aug 2019
Deep Self-Learning From Noisy Labels Jiangfan Han Ping Luo Xiaogang Wang NoLa 19 277 0 06 Aug 2019
Teacher Supervises Students How to Learn From Partially Labeled Images for Facial Landmark Detection Xuanyi Dong Yezhou Yang CVBM 14 66 0 06 Aug 2019
Product Image Recognition with Guidance Learning and Noisy Supervision Qing Li Xiaojiang Peng Liangliang Cao Wenbin Du Hao Xing Yu Qiao NoLa 9 21 0 26 Jul 2019
Deep network as memory space: complexity, generalization, disentangled representation and interpretability X. Dong L. Zhou 28 1 0 12 Jul 2019
Learning from Web Data with Self-Organizing Memory Module Y. Tu Li Niu Junjie Chen Dawei Cheng Liqing Zhang 20 20 0 28 Jun 2019
Data Cleansing for Models Trained with SGD Satoshi Hara Atsushi Nitanda Takanori Maehara TDI 31 68 0 20 Jun 2019
A Meta Approach to Defend Noisy Labels by the Manifold Regularizer PSDR Pengfei Chen B. Liao Guangyong Chen Shengyu Zhang NoLa 14 8 0 13 Jun 2019
Learning to Segment Skin Lesions from Noisy Annotations Z. Mirikharaji Yiqi Yan Ghassan Hamarneh 38 77 0 10 Jun 2019
Robust Learning Under Label Noise With Iterative Noise-Filtering D. Nguyen Thi-Phuong-Nhung Ngo Zhongyu Lou Michael Klar Laura Beggel Thomas Brox NoLa 6 16 0 01 Jun 2019
Are Anchor Points Really Indispensable in Label-Noise Learning? Xiaobo Xia Tongliang Liu N. Wang Bo Han Chen Gong Gang Niu Masashi Sugiyama NoLa 11 369 0 01 Jun 2019
Enhancing Simple Models by Exploiting What They Already Know Amit Dhurandhar Karthikeyan Shanmugam Ronny Luss 11 2 0 30 May 2019
Uncertainty Based Detection and Relabeling of Noisy Image Labels Jan M. Köhler Maximilian Autenrieth William H. Beluch NoLa 24 28 0 29 May 2019
Simple and Effective Regularization Methods for Training on Noisily Labeled Data with Generalization Guarantee Wei Hu Zhiyuan Li Dingli Yu NoLa 19 12 0 27 May 2019
Derivative Manipulation for General Example Weighting Xinshao Wang Elyor Kodirov Yang Hua N. Robertson NoLa 11 1 0 27 May 2019
Learning to Auto Weight: Entirely Data-driven and Highly Efficient Weighting Framework Zhenmao Li Yichao Wu Ken Chen Yudong Wu Shunfeng Zhou Jiaheng Liu Junjie Yan 13 5 0 27 May 2019
Countering Noisy Labels By Learning From Auxiliary Clean Labels Tsung Wei Tsai Chongxuan Li Jun Zhu SSL 10 1 0 23 May 2019
Training Object Detectors With Noisy Data Simon Chadwick Paul Newman NoLa 20 36 0 17 May 2019
Addressing the Loss-Metric Mismatch with Adaptive Loss Alignment Chen Huang Shuangfei Zhai Walter A. Talbott Miguel Angel Bautista Shi Sun Carlos Guestrin J. Susskind 26 75 0 15 May 2019
Task-Driven Data Verification via Gradient Descent Siavash Golkar Kyunghyun Cho 9 0 0 14 May 2019
Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels Pengfei Chen B. Liao Guangyong Chen Shengyu Zhang NoLa 9 383 0 13 May 2019
Label-Noise Robust Multi-Domain Image-to-Image Translation Takuhiro Kaneko Tatsuya Harada 30 3 0 06 May 2019
Unsupervised Label Noise Modeling and Loss Correction Eric Arazo Sanchez Diego Ortego Paul Albert Noel E. O'Connor Kevin McGuinness NoLa 44 602 0 25 Apr 2019
Reliable Weakly Supervised Learning: Maximize Gain and Maintain Safeness Lan-Zhe Guo Yu-Feng Li Ming Li Jinfeng Yi Bowen Zhou Zhi-Hua Zhou 19 2 0 22 Apr 2019
Least Squares Auto-Tuning Shane T. Barratt Stephen P. Boyd MoMe 19 23 0 10 Apr 2019
Wasserstein Adversarial Regularization (WAR) on label noise Kilian Fatras B. Bushan Sylvain Lobry Rémi Flamary D. Tuia Nicolas Courty 19 24 0 08 Apr 2019
Learning to Adapt for Stereo A. Tonioni Oscar Rahnama Thomas Joy Luigi Di Stefano Thalaiyasingam Ajanthan Philip Torr 13 83 0 05 Apr 2019
A Robust Learning Approach to Domain Adaptive Object Detection Mehran Khodabandeh Arash Vahdat Mani Ranjbar W. Macready ObjD OOD TTA 16 245 0 04 Apr 2019
IMAE for Noise-Robust Learning: Mean Absolute Error Does Not Treat Examples Equally and Gradient Magnitude's Variance Matters Xinshao Wang Yang Hua Elyor Kodirov David A. Clifton N. Robertson NoLa 24 62 0 28 Mar 2019
Gradient Descent with Early Stopping is Provably Robust to Label Noise for Overparameterized Neural Networks Mingchen Li Mahdi Soltanolkotabi Samet Oymak NoLa 47 351 0 27 Mar 2019
Noise-Tolerant Paradigm for Training Face Recognition CNNs Wei Hu Yangyu Huang Fan Zhang Ruirui Li NoLa CVBM 22 62 0 25 Mar 2019
Safeguarded Dynamic Label Regression for Generalized Noisy Supervision Jiangchao Yao Ya Zhang Ivor W. Tsang Jun-wei Sun NoLa 14 1 0 06 Mar 2019
Deep Multi-modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges Di Feng Christian Haase-Schuetz Lars Rosenbaum Heinz Hertlein Claudius Gläser Fabian Duffhauss W. Wiesbeck Klaus C. J. Dietmayer 3DPC 41 987 0 21 Feb 2019
Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting Jun Shu Qi Xie Lixuan Yi Qian Zhao Sanping Zhou Zongben Xu Deyu Meng NoLa 12 4 0 20 Feb 2019
Learning to Generalize from Sparse and Underspecified Rewards Rishabh Agarwal Chen Liang Dale Schuurmans Mohammad Norouzi OffRL 43 97 0 19 Feb 2019
Learning From Noisy Labels By Regularized Estimation Of Annotator Confusion Ryutaro Tanno A. Saeedi S. Sankaranarayanan Daniel C. Alexander N. Silberman NoLa 27 228 0 10 Feb 2019
Robust Inference via Generative Classifiers for Handling Noisy Labels Kimin Lee Sukmin Yun Kibok Lee Honglak Lee Bo-wen Li Jinwoo Shin NoLa 30 134 0 31 Jan 2019