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Transfer learning aims to leverage models pre-trained on source data to efficiently adapt to target setting, where only limited data are available for model fine-tuning.
Metric entropy of the grassmann manifold
Alain Pajor · 1998
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A model of inductive bias learning
Jonathan Baxter · 2000
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Provable meta-learning of linear representations
Nilesh Tripuraneni, Chi Jin, and Michael I Jordan · 2002
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Exploiting task relatedness for multiple task learning
Shai Ben-David and Reba Schuller · 2003
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Semi-supervised learning using gaussian fields and harmonic functions
Xiaojin Zhu, Zoubin Ghahramani, and John D Lafferty · 2003
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Adversarial classification
Nilesh Dalvi, Pedro Domingos, Sumit Sanghai, and Deepak Verma · 2004
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Adversarial learning
Daniel Lowd and Christopher Meek · 2005
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Compressive sampling
Emmanuel J Candès et al · 2006
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On the theory of transfer learning: The importance of task diversity
Nilesh Tripuraneni, Michael I Jordan, and Chi Jin · 2006
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Compressive sensing [lecture notes]
Richard G Baraniuk · 2007
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Automated flower classification over a large number of classes
Maria-Elena Nilsback and Andrew Zisserman · 2008
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Introduction to nonparametric estimation
Alexandre B Tsybakov · 2008
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Semi-supervised learning (chapelle, o. et al., eds.; 2006)[book reviews]
Olivier Chapelle, Bernhard Scholkopf, and Alexander Zien · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
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Introduction to semi-supervised learning
Xiaojin Zhu and Andrew B Goldberg · 2009
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The lasso risk for gaussian matrices
Mohsen Bayati and Andrea Montanari · 2011
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Transfer learning by borrowing examples for multiclass object detection
Joseph Jaewhan Lim · 2012
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Cats and dogs
Omkar M Parkhi, Andrea Vedaldi, Andrew Zisserman, and CV Jawahar · 2012
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Fine-grained visual classification of aircraft
Subhransu Maji, Esa Rahtu, Juho Kannala, Matthew Blaschko, and Andrea Vedaldi · 2013
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Food-101–mining discriminative components with random forests
Lukas Bossard, Matthieu Guillaumin, and Luc Van Gool · 2014
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Decaf: A deep convolutional activation feature for generic visual recognition
Jeff Donahue, Yangqing Jia, Oriol Vinyals, Judy Hoffman, Ning Zhang, Eric Tzeng, and Trevor Darrell · 2014
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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
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Cnn features off-the-shelf: an astounding baseline for recognition
Ali Sharif Razavian, Hossein Azizpour, Josephine Sullivan, and Stefan Carlsson · 2014
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Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
Anh Nguyen, Jason Yosinski, and Jeff Clune · 2015
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Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al · 2015
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A useful variant of the davis–kahan theorem for statisticians
Yi Yu, Tengyao Wang, and Richard J Samworth · 2015
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Unlabeled data improves adversarial robustness
Yair Carmon, Aditi Raghunathan, Ludwig Schmidt, John C Duchi, and Percy S Liang · 2019
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Certified adversarial robustness via randomized smoothing
Jeremy M Cohen, Elan Rosenfeld, and J Zico Kolter · 2019
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Robustness (python library), 2019
Logan Engstrom, Andrew Ilyas, Hadi Salman, Shibani Santurkar, and Dimitris Tsipras · 2019
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Using pre-training can improve model robustness and uncertainty
Dan Hendrycks, Kimin Lee, and Mantas Mazeika · 2019
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Parameter-efficient transfer learning for nlp
Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly · 2019
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Minyoung Huh, Pulkit Agrawal, and Alexei A Efros · 2016
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Adversarial machine learning at scale
Alexey Kurakin, Ian Goodfellow, and Samy Bengio · 2016
Cited alongside, same era.
The benefit of multitask representation learning
Andreas Maurer, Massimiliano Pontil, and Bernardino Romera-Paredes · 2016
Cited alongside, same era.
Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning
Hoo-Chang Shin, Holger R Roth, Mingchen Gao, Le Lu, Ziyue Xu, Isabella Nogues, Jianhua Yao, Daniel Mollura, and Ronald M Summers · 2016
Cited alongside, same era.
Towards evaluating the robustness of neural networks
Nicholas Carlini and David Wagner · 2017
Cited alongside, same era.
Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2017
Cited alongside, same era.
False discoveries occur early on the lasso path
Weijie Su, Małgorzata Bogdan, Emmanuel Candes, et al · 2017
Cited alongside, same era.
Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Joan Puigcerver, Jessica Yung, Sylvain Gelly, and Neil Houlsby · 2019
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Certified robustness to adversarial examples with differential privacy
Mathias Lecuyer, Vaggelis Atlidakis, Roxana Geambasu, Daniel Hsu, and Suman Jana · 2019
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Transfusion: Understanding transfer learning for medical imaging
Maithra Raghu, Chiyuan Zhang, Jon Kleinberg, and Samy Bengio · 2019
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Are labels required for improving adversarial robustness?
Robert Stanforth, Alhussein Fawzi, Pushmeet Kohli, et al · 2019
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High-dimensional statistics: A non-asymptotic viewpoint , volume 48
Martin J Wainwright · 2019
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Theoretically principled trade-off between robustness and accuracy
Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric Xing, Laurent El Ghaoui, and Michael Jordan · 2019
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Certified defenses for adversarial patches
Ping-Yeh Chiang, Renkun Ni, Ahmed Abdelkader, Chen Zhu, Christoph Studor, and Tom Goldstein · 2020
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Few-shot learning via learning the representation, provably
Simon S Du, Wei Hu, Sham M Kakade, Jason D Lee, and Qi Lei · 2020
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Enhancing certified robustness of smoothed classifiers via weighted model ensembling
Chizhou Liu, Yunzhen Feng, Ranran Wang, and Bin Dong · 2020
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Do adversarially robust imagenet models transfer better?
Hadi Salman, Andrew Ilyas, Logan Engstrom, Ashish Kapoor, and Aleksander Madry · 2020
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Adversarially-trained deep nets transfer better
Francisco Utrera, Evan Kravitz, N Benjamin Erichson, Rajiv Khanna, and Michael W Mahoney · 2020
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How does mixup help with robustness and generalization?
Linjun Zhang, Zhun Deng, Kenji Kawaguchi, Amirata Ghorbani, and James Zou · 2020
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Multispecies bioacoustic classification using transfer learning of deep convolutional neural networks with pseudo-labeling
Ming Zhong, Jack LeBien, Marconi Campos-Cerqueira, Rahul Dodhia, Juan Lavista Ferres, Julian P Velev, and T Mitchell Aide · 2020
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Improving adversarial robustness via unlabeled out-of-domain data
Zhun Deng, Linjun Zhang, Amirata Ghorbani, and James Zou · 2021
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A systematic evaluation of transfer learning and pseudo-labeling with bert-based ranking models
Iurii Mokrii, Leonid Boytsov, and Pavel Braslavski · 2021
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