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We study the ability of foundation models to learn representations for classification that are transferable to new, unseen classes.
Med3d: Transfer learning for 3d medical image analysis
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Jonathan Baxter · 2000
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Neural collapse with unconstrained features
Dustin G. Mixon, Hans Parshall, and Jianzong Pi · 2011
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Deep learning of representations for unsupervised and transfer learning
Yoshua Bengio · 2012
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Alex Krizhevsky · 2012
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On the importance of initialization and momentum in deep learning
Ilya Sutskever, James Martens, George E. Dahl, and Geoffrey E. Hinton · 2013
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A pac-bayesian bound for lifelong learning
Anastasia Pentina and Christoph Lampert · 2014
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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How transferable are features in deep neural networks?
Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson · 2014
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Fully convolutional networks for semantic segmentation
Jonathan Long, Evan Shelhamer, and Trevor Darrell · 2015
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Norm-based capacity control in neural networks
Behnam Neyshabur, Ryota Tomioka, and Nathan Srebro · 2015
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Faster r-cnn: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun · 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, Alexander C. Berg, and Li Fei-Fei · 2015
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A theoretical framework for deep transfer learning
Tomer Galanti, Lior Wolf, and Tamir Hazan · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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The benefit of multitask representation learning
Andreas Maurer, Massimiliano Pontil, and Bernardino Romera-Paredes · 2016
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You only look once: Unified, real-time object detection
Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi · 2016
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Matching networks for one shot learning
Oriol Vinyals, Charles Blundell, Timothy Lillicrap, koray kavukcuoglu, and Daan Wierstra · 2016
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Sergey Zagoruyko and N. Komodakis · 2016
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Spectrally-normalized margin bounds for neural networks
Peter L. Bartlett, Dylan J. Foster, and Matus Telgarsky · 2017
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EMNIST: an extension of MNIST to handwritten letters
Gregory Cohen, Saeed Afshar, Jonathan Tapson, and André van Schaik · 2017
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Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
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Size-independent sample complexity of neural networks
Noah Golowich, Alexander Rakhlin, and Ohad Shamir · 2017
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Mask r-cnn
Kaiming He, Georgia Gkioxari, Piotr Dollar, and Ross Girshick · 2017
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Mobilenets: Efficient convolutional neural networks for mobile vision applications, 2017
Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam · 2017
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Optimization as a model for few-shot learning
Meta-learning with differentiable convex optimization
Kwonjoon Lee, Subhransu Maji, Avinash Ravichandran, and Stefano Soatto · 2019
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Learning to propagate labels: Transductive propagation network for few-shot learning
Yanbin Liu, Juho Lee, Minseop Park, Saehoon Kim, Eunho Yang, Sungju Hwang, and Yi Yang · 2019
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Uniform concentration and symmetrization for weak interactions
Andreas Maurer and M. Pontil · 2019
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Transductive episodic-wise adaptive metric for few-shot learning
Limeng Qiao, Yemin Shi, Jia Li, Yaowei Wang, Tiejun Huang, and Yonghong Tian · 2019
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Few-shot learning with embedded class models and shot-free meta training
Avinash Ravichandran, Rahul Bhotika, and Stefano Soatto · 2019
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Meta-learning with latent embedding optimization
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Sachin Ravi and Hugo Larochelle · 2017
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Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard Zemel · 2017
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Encoder-decoder with atrous separable convolution for semantic image segmentation
Liang-Chieh Chen, Yukun Zhu, George Papandreou, Florian Schroff, and Hartwig Adam · 2018
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Dynamic few-shot visual learning without forgetting
Spyros Gidaris and Nikos Komodakis · 2018
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On tighter generalization bound for deep neural networks: CNNs, ResNets, and beyond, 2018
Xingguo Li, Junwei Lu, Zhaoran Wang, Jarvis Haupt, and Tuo Zhao · 2018
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A simple neural attentive meta-learner
Nikhil Mishra, Mostafa Rohaninejad, Xi Chen, and Pieter Abbeel · 2018
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Foundations of Machine Learning
Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar · 2018
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Andrei A. Rusu, Dushyant Rao, Jakub Sygnowski, Oriol Vinyals, Razvan Pascanu, Simon Osindero, and Raia Hadsell · 2019
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Meta-transfer learning for few-shot learning
Qianru Sun, Yaoyao Liu, Tat-Seng Chua, and Bernt Schiele · 2019
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High-dimensional statistics : a non-asymptotic viewpoint
Martin Wainwright · 2019
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2020
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A baseline for few-shot image classification
Guneet Singh Dhillon, Pratik Chaudhari, Avinash Ravichandran, and Stefano Soatto · 2020
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Unraveling meta-learning: Understanding feature representations for few-shot tasks
Micah Goldblum, Steven Reich, Liam Fowl, Renkun Ni, Valeriia Cherepanova, and Tom Goldstein · 2020
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Interpretable and accurate fine-grained recognition via region grouping
Zixuan Huang and Yin Li · 2020
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Prevalence of neural collapse during the terminal phase of deep learning training
Vardan Papyan, X. Y. Han, and David L. Donoho · 2020
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Rethinking few-shot image classification: A good embedding is all you need?
Yonglong Tian, Yue Wang, Dilip Krishnan, Joshua B. Tenenbaum, and Phillip Isola · 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, Erik Brynjolfsson, Shyamal Buch, Dallas Card, Rodrigo Castellon, Niladri Chatterji, Annie S. Chen, Kathleen Creel, Jared Quincy Davis, Dorottya Demszky, Chris Donahue, Moussa Doumbouya, Esin Durmus, Stefano Ermon, John Etchemendy, Kawin Ethayarajh, Li Fei-Fei, Chelsea Finn, Trevor Gale, Lauren Gillespie, Karan Goel, Noah D. Goodman, Shelby Grossman, Neel Guha, Tatsunori Hashimoto, Peter Henderson, John Hewitt, Daniel E. Ho, Jenny Hong, Kyle Hsu, Jing Huang, Thomas Icard, Saahil Jain, Dan Jurafsky, Pratyusha Kalluri, Siddharth Karamcheti, Geoff Keeling, Fereshte Khani, Omar Khattab, Pang Wei Koh, Mark S. Krass, Ranjay Krishna, Rohith Kuditipudi, and et al · 2021
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Few-shot learning via learning the representation, provably
Simon Shaolei Du, Wei Hu, Sham M. Kakade, Jason D. Lee, and Qi Lei · 2021
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Zero-shot text-to-image generation
Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever · 2021
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Dynamics and neural collapse in deep classifiers trained with the square loss
Mengjia Xu, Akshay Rangamani, Andrzej Banburski, Qianli Liao, Tomer Galanti, and Tomaso Poggio · 2021
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A geometric analysis of neural collapse with unconstrained features
Zhihui Zhu, Tianyu DING, Jinxin Zhou, Xiao Li, Chong You, Jeremias Sulam, and Qing Qu · 2021
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Extended unconstrained features model for exploring deep neural collapse
Tom Tirer and Joan Bruna · 2022
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