暂无分享,去创建一个
Tengyu Ma | Yann Dauphin | Lunjia Hu | Jiaming Song | Michael Auli | Tengyu Ma | Yann Dauphin | Michael Auli | Jiaming Song | Lunjia Hu
[1] Chen Sun,et al. Revisiting Unreasonable Effectiveness of Data in Deep Learning Era , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[2] Jun Sun,et al. Safeguarded Dynamic Label Regression for Noisy Supervision , 2019, AAAI.
[3] Michael S. Bernstein,et al. Embracing Error to Enable Rapid Crowdsourcing , 2016, CHI.
[4] Dumitru Erhan,et al. Training Deep Neural Networks on Noisy Labels with Bootstrapping , 2014, ICLR.
[5] Sergey Ioffe,et al. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.
[6] Jian Sun,et al. Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[7] Hongyi Zhang,et al. mixup: Beyond Empirical Risk Minimization , 2017, ICLR.
[8] Stefano Soatto,et al. Stochastic Gradient Descent Performs Variational Inference, Converges to Limit Cycles for Deep Networks , 2017, 2018 Information Theory and Applications Workshop (ITA).
[9] Behnam Neyshabur,et al. Implicit Regularization in Deep Learning , 2017, ArXiv.
[10] Michael S. Bernstein,et al. ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.
[11] Sergey Ioffe,et al. Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning , 2016, AAAI.
[12] Nir Shavit,et al. Deep Learning is Robust to Massive Label Noise , 2017, ArXiv.
[13] Yoshua Bengio,et al. A Closer Look at Memorization in Deep Networks , 2017, ICML.
[14] Jürgen Schmidhuber,et al. Simplifying Neural Nets by Discovering Flat Minima , 1994, NIPS.
[15] Kaiming He,et al. Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour , 2017, ArXiv.
[16] Aritra Ghosh,et al. Robust Loss Functions under Label Noise for Deep Neural Networks , 2017, AAAI.
[17] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[18] Samy Bengio,et al. Understanding deep learning requires rethinking generalization , 2016, ICLR.
[19] Abhinav Gupta,et al. Training Region-Based Object Detectors with Online Hard Example Mining , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[20] Abhinav Gupta,et al. Learning from Noisy Large-Scale Datasets with Minimal Supervision , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[21] Hongyang Zhang,et al. Algorithmic Regularization in Over-parameterized Matrix Sensing and Neural Networks with Quadratic Activations , 2017, COLT.
[22] Jorge Nocedal,et al. On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima , 2016, ICLR.
[23] Elad Hoffer,et al. Train longer, generalize better: closing the generalization gap in large batch training of neural networks , 2017, NIPS.
[24] Kaiming He,et al. Exploring the Limits of Weakly Supervised Pretraining , 2018, ECCV.
[25] Wei Li,et al. WebVision Database: Visual Learning and Understanding from Web Data , 2017, ArXiv.
[26] Xiaogang Wang,et al. Learning from massive noisy labeled data for image classification , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[27] Weilin Huang,et al. CurriculumNet: Weakly Supervised Learning from Large-Scale Web Images , 2018, ECCV.
[28] Kiyoharu Aizawa,et al. Joint Optimization Framework for Learning with Noisy Labels , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[29] Wei Hu,et al. Algorithmic Regularization in Learning Deep Homogeneous Models: Layers are Automatically Balanced , 2018, NeurIPS.
[30] Li Fei-Fei,et al. MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels , 2017, ICML.
[31] Stefano Soatto,et al. Entropy-SGD: biasing gradient descent into wide valleys , 2016, ICLR.
[32] Mert R. Sabuncu,et al. Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels , 2018, NeurIPS.
[33] Nikos Komodakis,et al. Wide Residual Networks , 2016, BMVC.
[34] Yuanzhi Li,et al. An Alternative View: When Does SGD Escape Local Minima? , 2018, ICML.
[35] Bin Yang,et al. Learning to Reweight Examples for Robust Deep Learning , 2018, ICML.
[36] Joan Bruna,et al. Training Convolutional Networks with Noisy Labels , 2014, ICLR 2014.
[37] Salvatore J. Stolfo,et al. Casting out Demons: Sanitizing Training Data for Anomaly Sensors , 2008, 2008 IEEE Symposium on Security and Privacy (sp 2008).
[38] 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.
[39] David M. Blei,et al. Stochastic Gradient Descent as Approximate Bayesian Inference , 2017, J. Mach. Learn. Res..
[40] Kevin Gimpel,et al. Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise , 2018, NeurIPS.
[41] Dacheng Tao,et al. Classification with Noisy Labels by Importance Reweighting , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[42] Frank Hutter,et al. SGDR: Stochastic Gradient Descent with Warm Restarts , 2016, ICLR.
[43] Tailin Wu,et al. Learning with Confident Examples: Rank Pruning for Robust Classification with Noisy Labels , 2017, UAI.
[44] 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).
[45] Tomas Pfister,et al. A Simple yet Effective Baseline for Robust Deep Learning with Noisy Labels , 2019, ArXiv.
[46] Carla E. Brodley,et al. Identifying and Eliminating Mislabeled Training Instances , 1996, AAAI/IAAI, Vol. 1.