PARS: Pseudo-Label Aware Robust Sample Selection for Learning with Noisy Labels
暂无分享,去创建一个
[1] Aritra Ghosh,et al. Robust Loss Functions under Label Noise for Deep Neural Networks , 2017, AAAI.
[2] David Berthelot,et al. MixMatch: A Holistic Approach to Semi-Supervised Learning , 2019, NeurIPS.
[3] Noel E. O'Connor,et al. Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning , 2019, 2020 International Joint Conference on Neural Networks (IJCNN).
[4] Richard Nock,et al. Making Deep Neural Networks Robust to Label Noise: A Loss Correction Approach , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[5] Hongyi Zhang,et al. mixup: Beyond Empirical Risk Minimization , 2017, ICLR.
[6] Li Fei-Fei,et al. ImageNet: A large-scale hierarchical image database , 2009, CVPR.
[7] Shai Shalev-Shwartz,et al. Decoupling "when to update" from "how to update" , 2017, NIPS.
[8] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[9] James Bailey,et al. Symmetric Cross Entropy for Robust Learning With Noisy Labels , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[10] Svetlana Lazebnik,et al. Flickr30k Entities: Collecting Region-to-Phrase Correspondences for Richer Image-to-Sentence Models , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[11] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[12] Frank Hutter,et al. SGDR: Stochastic Gradient Descent with Warm Restarts , 2016, ICLR.
[13] James Bailey,et al. Normalized Loss Functions for Deep Learning with Noisy Labels , 2020, ICML.
[14] Noel E. O'Connor,et al. Unsupervised label noise modeling and loss correction , 2019, ICML.
[15] Kilian Q. Weinberger,et al. Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[16] Xiaogang Wang,et al. Learning from massive noisy labeled data for image classification , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[17] Natalia Gimelshein,et al. PyTorch: An Imperative Style, High-Performance Deep Learning Library , 2019, NeurIPS.
[18] Nagarajan Natarajan,et al. Learning with Noisy Labels , 2013, NIPS.
[19] Kun Yi,et al. Probabilistic End-To-End Noise Correction for Learning With Noisy Labels , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[20] Yoshua Bengio,et al. A Closer Look at Memorization in Deep Networks , 2017, ICML.
[21] Xiaogang Wang,et al. Deep Self-Learning From Noisy Labels , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[22] Quoc V. Le,et al. AutoAugment: Learning Augmentation Strategies From Data , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[23] Li Fei-Fei,et al. MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels , 2017, ICML.
[24] Swami Sankaranarayanan,et al. Learning From Noisy Labels by Regularized Estimation of Annotator Confusion , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[25] Jian Sun,et al. Identity Mappings in Deep Residual Networks , 2016, ECCV.
[26] Alex Krizhevsky,et al. Learning Multiple Layers of Features from Tiny Images , 2009 .
[27] Haibo Hu,et al. Preserving User Privacy for Machine Learning: Local Differential Privacy or Federated Machine Learning? , 2020, IEEE Intelligent Systems.
[28] David Berthelot,et al. ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring , 2019, ArXiv.
[29] Luc Van Gool,et al. The Pascal Visual Object Classes (VOC) Challenge , 2010, International Journal of Computer Vision.
[30] Xingrui Yu,et al. Co-teaching: Robust training of deep neural networks with extremely noisy labels , 2018, NeurIPS.
[31] Dong-Hyun Lee,et al. Pseudo-Label : The Simple and Efficient Semi-Supervised Learning Method for Deep Neural Networks , 2013 .
[32] Elyor Kodirov,et al. IMAE for Noise-Robust Learning: Mean Absolute Error Does Not Treat Examples Equally and Gradient Magnitude's Variance Matters , 2019 .
[33] Dumitru Erhan,et al. Training Deep Neural Networks on Noisy Labels with Bootstrapping , 2014, ICLR.
[34] Quoc V. Le,et al. Randaugment: Practical automated data augmentation with a reduced search space , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[35] Xingrui Yu,et al. How does Disagreement Help Generalization against Label Corruption? , 2019, ICML.
[36] Jae-Gil Lee,et al. SELFIE: Refurbishing Unclean Samples for Robust Deep Learning , 2019, ICML.
[37] Yi Ding,et al. Augmentation Strategies for Learning with Noisy Labels , 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[38] Kiyoharu Aizawa,et al. Joint Optimization Framework for Learning with Noisy Labels , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[39] Gang Niu,et al. Are Anchor Points Really Indispensable in Label-Noise Learning? , 2019, NeurIPS.
[40] Jacob Goldberger,et al. Training deep neural-networks using a noise adaptation layer , 2016, ICLR.
[41] Sarvar Patel,et al. Practical Secure Aggregation for Privacy-Preserving Machine Learning , 2017, IACR Cryptol. ePrint Arch..
[42] Junnan Li,et al. DivideMix: Learning with Noisy Labels as Semi-supervised Learning , 2020, ICLR.
[43] Junmo Kim,et al. Joint Negative and Positive Learning for Noisy Labels , 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[44] Junmo Kim,et al. NLNL: Negative Learning for Noisy Labels , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[45] Weilong Yang,et al. Beyond Synthetic Noise: Deep Learning on Controlled Noisy Labels , 2019, ICML.
[46] Pietro Perona,et al. Microsoft COCO: Common Objects in Context , 2014, ECCV.
[47] David Berthelot,et al. FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence , 2020, NeurIPS.
[48] Mohan S. Kankanhalli,et al. Learning to Learn From Noisy Labeled Data , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[49] Samy Bengio,et al. Understanding deep learning requires rethinking generalization , 2016, ICLR.
[50] Sheng Liu,et al. Early-Learning Regularization Prevents Memorization of Noisy Labels , 2020, NeurIPS.
[51] Mert R. Sabuncu,et al. Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels , 2018, NeurIPS.