暂无分享,去创建一个
[1] James Bailey,et al. Dimensionality-Driven Learning with Noisy Labels , 2018, ICML.
[2] Xingrui Yu,et al. How does Disagreement Help Generalization against Label Corruption? , 2019, ICML.
[3] Yoshua Bengio,et al. A Closer Look at Memorization in Deep Networks , 2017, ICML.
[4] Samy Bengio,et al. Understanding deep learning requires rethinking generalization , 2016, ICLR.
[5] Xingrui Yu,et al. Co-teaching: Robust training of deep neural networks with extremely noisy labels , 2018, NeurIPS.
[6] Jeff A. Bilmes,et al. Combating Label Noise in Deep Learning Using Abstention , 2019, ICML.
[7] Deliang Fan,et al. A Semi-Supervised Two-Stage Approach to Learning from Noisy Labels , 2018, 2018 IEEE Winter Conference on Applications of Computer Vision (WACV).
[8] Geoffrey E. Hinton,et al. Regularizing Neural Networks by Penalizing Confident Output Distributions , 2017, ICLR.
[9] Christoph H. Lampert,et al. Robust Learning from Untrusted Sources , 2019, ICML.
[10] Woo-Jin Song,et al. Recycling: Semi-Supervised Learning With Noisy Labels in Deep Neural Networks , 2019, IEEE Access.
[11] Bin Yang,et al. Learning to Reweight Examples for Robust Deep Learning , 2018, ICML.
[12] Yanyao Shen,et al. Learning with Bad Training Data via Iterative Trimmed Loss Minimization , 2018, ICML.
[13] Geoffrey E. Hinton,et al. Visualizing Data using t-SNE , 2008 .
[14] 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).
[15] Pengfei Chen,et al. Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels , 2019, ICML.
[16] Yoshua Bengio,et al. Semi-supervised Learning by Entropy Minimization , 2004, CAP.
[17] 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).
[18] Arash Vahdat,et al. Toward Robustness against Label Noise in Training Deep Discriminative Neural Networks , 2017, NIPS.
[19] Harri Valpola,et al. Weight-averaged consistency targets improve semi-supervised deep learning results , 2017, ArXiv.
[20] Xiaogang Wang,et al. Learning from massive noisy labeled data for image classification , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[21] Abhinav Gupta,et al. Learning from Noisy Large-Scale Datasets with Minimal Supervision , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[22] Shin Ishii,et al. Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[23] Noel E. O'Connor,et al. Unsupervised label noise modeling and loss correction , 2019, 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] Shai Shalev-Shwartz,et al. Decoupling "when to update" from "how to update" , 2017, NIPS.
[26] Wei Li,et al. WebVision Database: Visual Learning and Understanding from Web Data , 2017, ArXiv.
[27] Lei Zhang,et al. CleanNet: Transfer Learning for Scalable Image Classifier Training with Label Noise , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[28] Jordi Pont-Tuset,et al. The Open Images Dataset V4 , 2018, International Journal of Computer Vision.
[29] Jacob Goldberger,et al. Training deep neural-networks using a noise adaptation layer , 2016, ICLR.
[30] Mohan S. Kankanhalli,et al. Learning to Learn From Noisy Labeled Data , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[31] Sergey Ioffe,et al. Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning , 2016, AAAI.
[32] Haim H. Permuter,et al. A study of Gaussian mixture models of color and texture features for image classification and segmentation , 2006, Pattern Recognit..
[33] Hongyi Zhang,et al. mixup: Beyond Empirical Risk Minimization , 2017, ICLR.
[34] Kaiming He,et al. Exploring the Limits of Weakly Supervised Pretraining , 2018, ECCV.
[35] Kiyoharu Aizawa,et al. Joint Optimization Framework for Learning with Noisy Labels , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[36] Jian Sun,et al. Identity Mappings in Deep Residual Networks , 2016, ECCV.
[37] Dumitru Erhan,et al. Training Deep Neural Networks on Noisy Labels with Bootstrapping , 2014, ICLR.
[38] Yale Song,et al. Learning from Noisy Labels with Distillation , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[39] David Berthelot,et al. MixMatch: A Holistic Approach to Semi-Supervised Learning , 2019, NeurIPS.
[40] Le Song,et al. Iterative Learning with Open-set Noisy Labels , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[41] Kevin Gimpel,et al. Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise , 2018, NeurIPS.
[42] Yali Wang,et al. MetaCleaner: Learning to Hallucinate Clean Representations for Noisy-Labeled Visual Recognition , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).