Supervised Contrastive Learning
暂无分享,去创建一个
Ce Liu | Yonglong Tian | Dilip Krishnan | Phillip Isola | Piotr Teterwak | Chen Wang | Aaron Sarna | Aaron Maschinot | Prannay Khosla
[1] Chen Sun,et al. What makes for good views for contrastive learning , 2020, NeurIPS.
[2] Phillip Isola,et al. Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere , 2020, ICML.
[3] Geoffrey E. Hinton,et al. A Simple Framework for Contrastive Learning of Visual Representations , 2020, ICML.
[4] S. Gelly,et al. Big Transfer (BiT): General Visual Representation Learning , 2019, ECCV.
[5] Ross B. Girshick,et al. Momentum Contrast for Unsupervised Visual Representation Learning , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[6] Quoc V. Le,et al. Self-Training With Noisy Student Improves ImageNet Classification , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[7] 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).
[8] Michael Tschannen,et al. On Mutual Information Maximization for Representation Learning , 2019, ICLR.
[9] Phillip Isola,et al. Contrastive Multiview Coding , 2019, ECCV.
[10] Ali Razavi,et al. Data-Efficient Image Recognition with Contrastive Predictive Coding , 2019, ICML.
[11] S. Gelly,et al. Large Scale Learning of General Visual Representations for Transfer , 2019, ArXiv.
[12] Quoc V. Le,et al. RandAugment: Practical data augmentation with no separate search , 2019, ArXiv.
[13] Yiming Yang,et al. XLNet: Generalized Autoregressive Pretraining for Language Understanding , 2019, NeurIPS.
[14] Colin Wei,et al. Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss , 2019, NeurIPS.
[15] Geoffrey E. Hinton,et al. When Does Label Smoothing Help? , 2019, NeurIPS.
[16] Quoc V. Le,et al. AutoAugment: Learning Augmentation Strategies From Data , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[17] Seong Joon Oh,et al. CutMix: Regularization Strategy to Train Strong Classifiers With Localizable Features , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[18] Geoffrey E. Hinton,et al. Analyzing and Improving Representations with the Soft Nearest Neighbor Loss , 2019, ICML.
[19] S. Shankar Sastry,et al. Cross-Entropy Loss and Low-Rank Features Have Responsibility for Adversarial Examples , 2019, ArXiv.
[20] Kaiming He,et al. Rethinking ImageNet Pre-Training , 2018, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[21] Yoshua Bengio,et al. Learning deep representations by mutual information estimation and maximization , 2018, ICLR.
[22] Thomas G. Dietterich,et al. Benchmarking Neural Network Robustness to Common Corruptions and Perturbations , 2018, ICLR.
[23] Quoc V. Le,et al. Do Better ImageNet Models Transfer Better? , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[24] Ming-Wei Chang,et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.
[25] R. Sarpong,et al. Bio-inspired synthesis of xishacorenes A, B, and C, and a new congener from fuscol† †Electronic supplementary information (ESI) available. See DOI: 10.1039/c9sc02572c , 2019, Chemical science.
[26] Alexei A. Efros,et al. Improving Generalization via Scalable Neighborhood Component Analysis , 2018, ECCV.
[27] Oriol Vinyals,et al. Representation Learning with Contrastive Predictive Coding , 2018, ArXiv.
[28] Loïc Le Folgoc,et al. Semi-Supervised Learning via Compact Latent Space Clustering , 2018, ICML.
[29] Stella X. Yu,et al. Unsupervised Feature Learning via Non-parametric Instance Discrimination , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[30] Mert R. Sabuncu,et al. Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels , 2018, NeurIPS.
[31] Hossein Mobahi,et al. Large Margin Deep Networks for Classification , 2018, NeurIPS.
[32] Hongyi Zhang,et al. mixup: Beyond Empirical Risk Minimization , 2017, ICLR.
[33] Sergey Levine,et al. Time-Contrastive Networks: Self-Supervised Learning from Video , 2017, 2018 IEEE International Conference on Robotics and Automation (ICRA).
[34] Yang You,et al. Large Batch Training of Convolutional Networks , 2017, 1708.03888.
[35] Yang You,et al. Scaling SGD Batch Size to 32K for ImageNet Training , 2017, ArXiv.
[36] Quoc V. Le,et al. Massive Exploration of Neural Machine Translation Architectures , 2017, EMNLP.
[37] Alexei A. Efros,et al. Split-Brain Autoencoders: Unsupervised Learning by Cross-Channel Prediction , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[38] Kihyuk Sohn,et al. Improved Deep Metric Learning with Multi-class N-pair Loss Objective , 2016, NIPS.
[39] Sebastian Ruder,et al. An overview of gradient descent optimization algorithms , 2016, Vestnik komp'iuternykh i informatsionnykh tekhnologii.
[40] Meng Yang,et al. Large-Margin Softmax Loss for Convolutional Neural Networks , 2016, ICML.
[41] Paolo Favaro,et al. Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles , 2016, ECCV.
[42] Alexei A. Efros,et al. Colorful Image Colorization , 2016, ECCV.
[43] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[44] Sergey Ioffe,et al. Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[45] Alexei A. Efros,et al. Unsupervised Visual Representation Learning by Context Prediction , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[46] James Philbin,et al. FaceNet: A unified embedding for face recognition and clustering , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[47] Geoffrey E. Hinton,et al. Distilling the Knowledge in a Neural Network , 2015, ArXiv.
[48] Bart De Moor,et al. Hyperparameter Search in Machine Learning , 2015, ArXiv.
[49] Kenneth W. Shum,et al. Deep Representation Learning with Target Coding , 2015, AAAI.
[50] Jonathon Shlens,et al. Explaining and Harnessing Adversarial Examples , 2014, ICLR.
[51] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[52] Joan Bruna,et al. Training Convolutional Networks with Noisy Labels , 2014, ICLR 2014.
[53] Koray Kavukcuoglu,et al. Learning word embeddings efficiently with noise-contrastive estimation , 2013, NIPS.
[54] Jeffrey Dean,et al. Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.
[55] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[56] Yoshua Bengio,et al. Random Search for Hyper-Parameter Optimization , 2012, J. Mach. Learn. Res..
[57] Aapo Hyvärinen,et al. Noise-contrastive estimation: A new estimation principle for unnormalized statistical models , 2010, AISTATS.
[58] Fei-Fei Li,et al. ImageNet: A large-scale hierarchical image database , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.
[59] Samy Bengio,et al. Large Scale Online Learning of Image Similarity Through Ranking , 2009, J. Mach. Learn. Res..
[60] Alex Krizhevsky,et al. Learning Multiple Layers of Features from Tiny Images , 2009 .
[61] Geoffrey E. Hinton,et al. Learning a Nonlinear Embedding by Preserving Class Neighbourhood Structure , 2007, AISTATS.
[62] Jianguo Zhang,et al. The PASCAL Visual Object Classes Challenge , 2006 .
[63] Luc Van Gool,et al. The 2005 PASCAL Visual Object Classes Challenge , 2005, MLCW.
[64] Kilian Q. Weinberger,et al. Distance Metric Learning for Large Margin Nearest Neighbor Classification , 2005, NIPS.
[65] Yann LeCun,et al. Learning a similarity metric discriminatively, with application to face verification , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).
[66] Eric R. Ziegel,et al. The Elements of Statistical Learning , 2003, Technometrics.
[67] Esther Levin,et al. Accelerated Learning in Layered Neural Networks , 1988, Complex Syst..
[68] Eric B. Baum,et al. Supervised Learning of Probability Distributions by Neural Networks , 1987, NIPS.
[69] Geoffrey E. Hinton,et al. Learning representations by back-propagating errors , 1986, Nature.
[70] J. Jensen. Sur les fonctions convexes et les inégalités entre les valeurs moyennes , 1906 .