Fetching the paper…
Reading the bibliography…
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
Sihong Chen, Kai Ma, and Yefeng Zheng · 1904
Earlier work this paper cites.
Learning many related tasks at the same time with backpropagation
Rich Caruana · 1995
Earlier work this paper cites.
A model of inductive bias learning
Jonathan Baxter · 2000
Earlier work this paper cites.
On the asymptotic representation of the euler gamma function by ramanujan
Ekatherina A. Karatsuba · 2001
Earlier work this paper cites.
Rademacher and gaussian complexities: Risk bounds and structural results
Peter L. Bartlett and Shahar Mendelson · 2002
Earlier work this paper cites.
Analysis of representations for domain adaptation
Shai Ben-David, John Blitzer, Koby Crammer, and Fernando Pereira · 2006
Earlier work this paper cites.
Learning theory, 2008
Sham Kakade and Ambuj Tewari · 2008
Earlier work this paper cites.
Domain adaptation: Learning bounds and algorithms
Yishay Mansour, Mehryar Mohri, and Afshin Rostamizadeh · 2009
Earlier work this paper cites.
Deep learning of representations for unsupervised and transfer learning
Yoshua Bengio · 2012
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky · 2012
Earlier work this paper cites.
A pac-bayesian bound for lifelong learning
Anastasia Pentina and Christoph Lampert · 2014
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
Earlier work this paper cites.
How transferable are features in deep neural networks?
Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson · 2014
Earlier work this paper cites.
Fully convolutional networks for semantic segmentation
Jonathan Long, Evan Shelhamer, and Trevor Darrell · 2015
Earlier work this paper cites.
Faster r-cnn: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun · 2015
Earlier work this paper cites.
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
Earlier work this paper cites.
A theoretical framework for deep transfer learning
Tomer Galanti, Lior Wolf, and Tamir Hazan · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
The benefit of multitask representation learning
Andreas Maurer, Massimiliano Pontil, and Bernardino Romera-Paredes · 2016
Earlier work this paper cites.
You only look once: Unified, real-time object detection
Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi · 2016
Earlier work this paper cites.
Matching networks for one shot learning
Oriol Vinyals, Charles Blundell, Timothy Lillicrap, koray kavukcuoglu, and Daan Wierstra · 2016
Earlier work this paper cites.
Sergey Zagoruyko and N. Komodakis · 2016
Cited alongside, same era.
Spectrally-normalized margin bounds for neural networks
Peter L. Bartlett, Dylan J. Foster, and Matus Telgarsky · 2017
Cited alongside, same era.
Emnist: an extension of mnist to handwritten letters
Gregory Cohen, Saeed Afshar, Jonathan Tapson, and André van Schaik · 2017
Cited alongside, same era.
Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
Cited alongside, same era.
Size-independent sample complexity of neural networks
Noah Golowich, Alexander Rakhlin, and Ohad Shamir · 2017
Cited alongside, same era.
Mask r-cnn
Adaptive gradient-based meta-learning methods
Mikhail Khodak, Maria-Florina F Balcan, and Ameet S Talwalkar · 2019
Later among the works it cites.
Meta-learning with differentiable convex optimization
Kwonjoon Lee, Subhransu Maji, Avinash Ravichandran, and Stefano Soatto · 2019
Later among the works it cites.
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
Later among the works it cites.
Uniform concentration and symmetrization for weak interactions
Andreas Maurer and M. Pontil · 2019
Later among the works it cites.
Transductive episodic-wise adaptive metric for few-shot learning
Limeng Qiao, Yemin Shi, Jia Li, Yaowei Wang, Tiejun Huang, and Yonghong Tian · 2019
Later among the works it cites.
Few-shot learning with embedded class models and shot-free meta training
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Kaiming He, Georgia Gkioxari, Piotr Dollar, and Ross Girshick · 2017
Cited alongside, same era.
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
Cited alongside, same era.
Optimization as a model for few-shot learning
S. Ravi and H. Larochelle · 2017
Cited alongside, same era.
Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard Zemel · 2017
Cited alongside, same era.
Encoder-decoder with atrous separable convolution for semantic image segmentation
Liang-Chieh Chen, Yukun Zhu, George Papandreou, Florian Schroff, and Hartwig Adam · 2018
Cited alongside, same era.
Dynamic few-shot visual learning without forgetting
Spyros Gidaris and Nikos Komodakis · 2018
Cited alongside, same era.
A simple neural attentive meta-learner
Nikhil Mishra, Mostafa Rohaninejad, Xi Chen, and Pieter Abbeel · 2018
Cited alongside, same era.
Avinash Ravichandran, Rahul Bhotika, and Stefano Soatto · 2019
Later among the works it cites.
Meta-learning with latent embedding optimization
Andrei A. Rusu, Dushyant Rao, Jakub Sygnowski, Oriol Vinyals, Razvan Pascanu, Simon Osindero, and Raia Hadsell · 2019
Later among the works it cites.
Meta-transfer learning for few-shot learning
Qianru Sun, Yaoyao Liu, Tat-Seng Chua, and Bernt Schiele · 2019
Later among the works it cites.
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
Later among the works it cites.
A baseline for few-shot image classification
Guneet Singh Dhillon, Pratik Chaudhari, Avinash Ravichandran, and Stefano Soatto · 2020
Later among the works it cites.
Interpretable and accurate fine-grained recognition via region grouping
Zixuan Huang and Yin Li · 2020
Later among the works it cites.
Neural collapse with unconstrained features, 2020
Dustin G. Mixon, Hans Parshall, and Jianzong Pi · 2020
Later among the works it cites.
Prevalence of neural collapse during the terminal phase of deep learning training
Vardan Papyan, X. Y. Han, and David L. Donoho · 2020
Later among the works it cites.
Theoretical issues in deep networks
Tomaso Poggio, Andrzej Banburski, and Qianli Liao · 2020
Later among the works it cites.
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
Later among the works it cites.
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
Closest in time.
Few-shot learning via learning the representation, provably
Simon Shaolei Du, Wei Hu, Sham M. Kakade, Jason D. Lee, and Qi Lei · 2021
Closest in time.
Neural collapse under mse loss: Proximity to and dynamics on the central path, 2021
X. Y. Han, Vardan Papyan, and David L. Donoho · 2021
Closest in time.
Zero-shot text-to-image generation
Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever · 2021
Closest in time.
Dynamics and neural collapse in deep classifiers trained with the square loss
Akshay Rangamani, Mengjia Xu, Andrzej Banburski, Qianli Liao, and Tomaso Poggio · 2021
Closest in time.