暂无分享,去创建一个
Cyrus Rashtchian | Ruslan Salakhutdinov | Kamalika Chaudhuri | Yao-Yuan Yang | R. Salakhutdinov | Kamalika Chaudhuri | Cyrus Rashtchian | Yao-Yuan Yang
[1] Fei-FeiLi,et al. One-Shot Learning of Object Categories , 2006 .
[2] Matthias Hein,et al. Towards neural networks that provably know when they don't know , 2020, ICLR.
[3] Shruti Tople,et al. Domain Generalization using Causal Matching , 2020, ICML.
[4] Gregory R. Koch,et al. Siamese Neural Networks for One-Shot Image Recognition , 2015 .
[5] Yoshua Bengio,et al. How transferable are features in deep neural networks? , 2014, NIPS.
[6] Aleksander Madry,et al. Towards Deep Learning Models Resistant to Adversarial Attacks , 2017, ICLR.
[7] Supriyo Chakraborty,et al. Improving Adversarial Robustness Through Progressive Hardening , 2020, ArXiv.
[8] Matthias Hein,et al. Provable Worst Case Guarantees for the Detection of Out-of-Distribution Data , 2020, ArXiv.
[9] Nikos Komodakis,et al. Wide Residual Networks , 2016, BMVC.
[10] Cyrus Rashtchian,et al. A Closer Look at Accuracy vs. Robustness , 2020, NeurIPS.
[11] Zhuowen Tu,et al. Aggregated Residual Transformations for Deep Neural Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[12] Jonathon Shlens,et al. Explaining and Harnessing Adversarial Examples , 2014, ICLR.
[13] Stefano Soatto,et al. A Baseline for Few-Shot Image Classification , 2019, ICLR.
[14] Gaël Varoquaux,et al. Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..
[15] Steffen Bickel,et al. Discriminative Learning Under Covariate Shift , 2009, J. Mach. Learn. Res..
[16] James T. Kwok,et al. Generalizing from a Few Examples , 2019, ACM Comput. Surv..
[17] Samy Bengio,et al. Adversarial Machine Learning at Scale , 2016, ICLR.
[18] Kibok Lee,et al. A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks , 2018, NeurIPS.
[19] David Wagner,et al. Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods , 2017, AISec@CCS.
[20] Jon Kleinberg,et al. Transfusion: Understanding Transfer Learning for Medical Imaging , 2019, NeurIPS.
[21] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[22] David Lopez-Paz,et al. Invariant Risk Minimization , 2019, ArXiv.
[23] R. Srikant,et al. Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks , 2017, ICLR.
[24] Malik Yousef,et al. One-Class SVMs for Document Classification , 2002, J. Mach. Learn. Res..
[25] Michael Fink,et al. Object Classification from a Single Example Utilizing Class Relevance Metrics , 2004, NIPS.
[26] Joan Bruna,et al. Intriguing properties of neural networks , 2013, ICLR.
[27] Dylan Hadfield-Menell,et al. Adversarial Training with Voronoi Constraints , 2019, ArXiv.
[28] Cyrus Rashtchian,et al. Adversarial Robustness Through Local Lipschitzness , 2020, ArXiv.
[29] Gang Yu,et al. High-Order Information Matters: Learning Relation and Topology for Occluded Person Re-Identification , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[30] Hugo Terashima-Marín,et al. Learning from Few Samples: A Survey , 2020, ArXiv.
[31] Pietro Perona,et al. One-shot learning of object categories , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[32] VARUN CHANDOLA,et al. Anomaly detection: A survey , 2009, CSUR.
[33] Alex Krizhevsky,et al. Learning Multiple Layers of Features from Tiny Images , 2009 .
[34] Michael S. Bernstein,et al. ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.
[35] Nathan Srebro,et al. Exploring Generalization in Deep Learning , 2017, NIPS.
[36] Pushmeet Kohli,et al. Adversarial Robustness through Local Linearization , 2019, NeurIPS.
[37] Michael I. Jordan,et al. Theoretically Principled Trade-off between Robustness and Accuracy , 2019, ICML.
[38] Hongyi Zhang,et al. mixup: Beyond Empirical Risk Minimization , 2017, ICLR.
[39] Michael W. Mahoney,et al. Adversarially-Trained Deep Nets Transfer Better , 2020, ArXiv.
[40] Lionel M. Ni,et al. Generalizing from a Few Examples , 2020, ACM Comput. Surv..
[41] Mei Wang,et al. Deep Visual Domain Adaptation: A Survey , 2018, Neurocomputing.
[42] Taghi M. Khoshgoftaar,et al. A survey on Image Data Augmentation for Deep Learning , 2019, Journal of Big Data.
[43] Yi Zhang,et al. Stronger generalization bounds for deep nets via a compression approach , 2018, ICML.
[44] Ashish Kapoor,et al. Do Adversarially Robust ImageNet Models Transfer Better? , 2020, NeurIPS.
[45] Pin-Yu Chen,et al. CAT: Customized Adversarial Training for Improved Robustness , 2020, IJCAI.
[46] John Schulman,et al. Concrete Problems in AI Safety , 2016, ArXiv.
[47] Jeff Johnson,et al. Billion-Scale Similarity Search with GPUs , 2017, IEEE Transactions on Big Data.
[48] Jasper Snoek,et al. Likelihood Ratios for Out-of-Distribution Detection , 2019, NeurIPS.
[49] Matthias Hein,et al. Certifiably Adversarially Robust Detection of Out-of-Distribution Data , 2020, NeurIPS.