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Deep learning is increasingly moving towards a transfer learning paradigm whereby large foundation models are fine-tuned on downstream tasks, starting from an initialization learned on the source task.
Automated flower classification over a large number of classes
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Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
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Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
Earlier work this paper cites.
The pascal visual object classes (voc) challenge
M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman · 2010
Earlier work this paper cites.
Bayesian learning via stochastic gradient langevin dynamics
Max Welling and Yee W Teh · 2011
Earlier work this paper cites.
Cats and dogs
Omkar M Parkhi, Andrea Vedaldi, Andrew Zisserman, and CV Jawahar · 2012
Earlier work this paper cites.
Stochastic gradient hamiltonian monte carlo
Tianqi Chen, Emily Fox, and Carlos Guestrin · 2014
Earlier work this paper cites.
Faster r-cnn: Towards real-time object detection with region proposal networks
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Earlier work this paper cites.
The cityscapes dataset for semantic urban scene understanding
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Earlier work this paper cites.
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Earlier work this paper cites.
Rethinking atrous convolution for semantic image segmentation
Liang-Chieh Chen, George Papandreou, Florian Schroff, and Hartwig Adam · 2017
Earlier work this paper cites.
Overcoming catastrophic forgetting in neural networks
James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, et al · 2017
Earlier work this paper cites.
Visualizing the loss landscape of neural nets
Hao Li, Zheng Xu, Gavin Taylor, Christoph Studer, and Tom Goldstein · 2017
Earlier work this paper cites.
Variational continual learning
Cuong V Nguyen, Yingzhen Li, Thang D Bui, and Richard E Turner · 2017
Earlier work this paper cites.
Encoder-decoder with atrous separable convolution for semantic image segmentation
Liang-Chieh Chen, Yukun Zhu, George Papandreou, Florian Schroff, and Hartwig Adam · 2018
Earlier work this paper cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
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Jeremy Howard and Sebastian Ruder · 2018
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Averaging weights leads to wider optima and better generalization
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Semi-supervised deep kernel learning: Regression with unlabeled data by minimizing predictive variance
Neal Jean, Sang Michael Xie, and Stefano Ermon · 2018
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Optimal bayesian transfer learning
Alireza Karbalayghareh, Xiaoning Qian, and Edward R Dougherty · 2018
Noise contrastive priors for functional uncertainty
Danijar Hafner, Dustin Tran, Timothy Lillicrap, Alex Irpan, and James Davidson · 2020
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Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2020
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Understanding generalization through visualizations
W Ronny Huang, Zeyad Emam, Micah Goldblum, Liam Fowl, Justin K Terry, Furong Huang, and Tom Goldstein · 2020
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Continual deep learning by functional regularisation of memorable past
Pingbo Pan, Siddharth Swaroop, Alexander Immer, Runa Eschenhagen, Richard Turner, and Mohammad Emtiyaz E Khan · 2020
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Bayesian meta-learning for the few-shot setting via deep kernels
Massimiliano Patacchiola, Jack Turner, Elliot J Crowley, Michael O’Boyle, and Amos J Storkey · 2020
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Bayesian deep learning and a probabilistic perspective of generalization
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Do cifar-10 classifiers generalize to cifar-10?
Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, and Vaishaal Shankar · 2018
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Natural variational continual learning
Hanna Tseran, Mohammad Emtiyaz Khan, Tatsuya Harada, and Thang D Bui · 2018
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Uncertainty-guided continual learning with bayesian neural networks
Sayna Ebrahimi, Mohamed Elhoseiny, Trevor Darrell, and Marcus Rohrbach · 2019
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A simple baseline for bayesian uncertainty in deep learning
Wesley J Maddox, Pavel Izmailov, Timur Garipov, Dmitry P Vetrov, and Andrew Gordon Wilson · 2019
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Cyclical stochastic gradient mcmc for bayesian deep learning
Ruqi Zhang, Chunyuan Li, Jianyi Zhang, Changyou Chen, and Andrew Gordon Wilson · 2019
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Bayesian neural multi-source transfer learning
Rohitash Chandra and Arpit Kapoor · 2020
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
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Andrew Gordon Wilson and Pavel Izmailov · 2020
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On the opportunities and risks of foundation models
Rishi Bommasani, Drew A Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, et al · 2021
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Coatnet: Marrying convolution and attention for all data sizes
Zihang Dai, Hanxiao Liu, Quoc V Le, and Mingxing Tan · 2021
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Bayesian neural network priors revisited
Vincent Fortuin, Adrià Garriga-Alonso, Florian Wenzel, Gunnar Rätsch, Richard Turner, Mark van der Wilk, and Laurence Aitchison · 2021
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Variational auto-regressive gaussian processes for continual learning
Sanyam Kapoor, Theofanis Karaletsos, and Thang D Bui · 2021
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Fast adaptation with linearized neural networks
Wesley Maddox, Shuai Tang, Pablo Moreno, Andrew Gordon Wilson, and Andreas Damianou · 2021
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Semi-supervised learning with bayesian confidence propagation neural network
Naresh Balaji Ravichandran, Anders Lansner, and Pawel Herman · 2021
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Continual learning via function-space variational inference
Tim GJ Rudner, Freddie Bickford Smith, Qixuan Feng, Yee Whye Teh, and Yarin Gal · 2021
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Bayesian transfer learning: An overview of probabilistic graphical models for transfer learning
Junyu Xuan, Jie Lu, and Guangquan Zhang · 2021
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