Fetching the paper…
Reading the bibliography…
Few-shot or one-shot learning of classifiers requires a significant inductive bias towards the type of task to be learned.
Evolutionary principles in self-referential learning, or on learning how to learn: the meta-meta-… hook
Jürgen Schmidhuber · 1987
Earlier work this paper cites.
Learning a synaptic learning rule
Yoshua Bengio, Samy Bengio, and Jocelyn Cloutier · 1990
Earlier work this paper cites.
Regularization theory and neural networks architectures
Federico Girosi, Michael Jones, and Tomaso Poggio · 1995
Earlier work this paper cites.
The elements of statistical learning
Jerome Friedman, Trevor Hastie, and Robert Tibshirani · 2001
Earlier work this paper cites.
Visualizing data using t-sne
Laurens van der Maaten and Geoffrey Hinton · 2008
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
One shot learning of simple visual concepts
Brenden Lake, Ruslan Salakhutdinov, Jason Gross, and Joshua Tenenbaum · 2011
Earlier work this paper cites.
Learning from data
Yaser S Abu-Mostafa, Malik Magdon-Ismail, and Hsuan-Tien Lin · 2012
Earlier work this paper cites.
UCF101: A dataset of 101 human actions classes from videos in the wild
Khurram Soomro, Amir Roshan Zamir, and Mubarak Shah · 2012
Earlier work this paper cites.
An introduction to statistical learning
Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani · 2013
Earlier work this paper cites.
Caffe: Convolutional architecture for fast feature embedding
Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross Girshick, Sergio Guadarrama, and Trevor Darrell · 2014
Earlier work this paper cites.
Large-scale video classification with convolutional neural networks
Andrej Karpathy, George Toderici, Sanketh Shetty, Thomas Leung, Rahul Sukthankar, and Li Fei-Fei · 2014
Earlier work this paper cites.
Large-scale video classification with convolutional neural networks
Andrej Karpathy, George Toderici, Sanketh Shetty, Thomas Leung, Rahul Sukthankar, and Li Fei-Fei · 2014
Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
Earlier work this paper cites.
Objects2action: Classifying and localizing actions without any video example
Mihir Jain, Jan C van Gemert, Thomas Mensink, and Cees GM Snoek · 2015
Earlier work this paper cites.
Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xinaogang Wang, and Xiaoou Tang · 2015
Cited alongside, same era.
Learning spatiotemporal features with 3d convolutional networks
Du Tran, Lubomir Bourdev, Rob Fergus, Lorenzo Torresani, and Manohar Paluri · 2015
Cited alongside, same era.
Using fast weights to attend to the recent past
Jimmy Ba, Geoffrey E Hinton, Volodymyr Mnih, Joel Z Leibo, and Catalin Ionescu · 2016
Cited alongside, same era.
InfoGAN: Interpretable representation learning by information maximizing generative adversarial nets
Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, and Pieter Abbeel · 2016
Cited alongside, same era.
Jeff Donahue, Philipp Krähenbühl, and Trevor Darrell · 2016
Cited alongside, same era.
Optimization as a model for few-shot learning
Understanding and improving interpolation in autoencoders via an adversarial regularizer
David Berthelot, Colin Raffel, Aurko Roy, and Ian Goodfellow · 2018
Closest in time.
Deep clustering for unsupervised learning of visual features
Mathilde Caron, Piotr Bojanowski, Armand Joulin, and Matthijs Douze · 2018
Closest in time.
Autoaugment: Learning augmentation policies from data
Ekin D Cubuk, Barret Zoph, Dandelion Mane, Vijay Vasudevan, and Quoc V Le · 2018
Closest in time.
Unsupervised meta-learning for reinforcement learning
Abhishek Gupta, Benjamin Eysenbach, Chelsea Finn, and Sergey Levine · 2018
Closest in time.
Unsupervised learning via meta-learning
Kyle Hsu, Sergey Levine, and Chelsea Finn · 2018
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Sachin Ravi and Hugo Larochelle · 2016
Cited alongside, same era.
Meta-learning with memory-augmented neural networks
Adam Santoro, Sergey Bartunov, Matthew Botvinick, Daan Wierstra, and Timothy Lillicrap · 2016
Cited alongside, same era.
Matching networks for one shot learning
Oriol Vinyals, Charles Blundell, Tim Lillicrap, and Daan Wierstra · 2016
Cited alongside, same era.
Understanding deep learning requires rethinking generalization
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals · 2016
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.
The kinetics human action video dataset
Will Kay, Joao Carreira, Karen Simonyan, Brian Zhang, Chloe Hillier, Sudheendra Vijayanarasimhan, Fabio Viola, Tim Green, Trevor Back, Paul Natsev, et al · 2017
Cited alongside, same era.
Label efficient learning of transferable representations across domains and tasks
Zelun Luo, Yuliang Zou, Judy Hoffman, and Li F Fei-Fei · 2017
Cited alongside, same era.
Closest in time.
Online learning of a memory for learning rates
Franziska Meier, Daniel Kappler, and Stefan Schaal · 2018
Closest in time.
Learning unsupervised learning rules
Luke Metz, Niru Maheswaranathan, Brian Cheung, and Jascha Sohl-Dickstein · 2018
Closest in time.
Differentiable plasticity: training plastic neural networks with backpropagation
Thomas Miconi, Jeff Clune, and Kenneth O Stanley · 2018
Closest in time.
A simple neural attentive meta-learner
Nikhil Mishra, Mostafa Rohaninejad, Xi Chen, and Pieter Abbeel · 2018
Closest in time.
Reptile: a scalable metalearning algorithm
Alex Nichol and John Schulman · 2018
Closest in time.
Realistic evaluation of deep semi-supervised learning algorithms
Avital Oliver, Augustus Odena, Colin A Raffel, Ekin Dogus Cubuk, and Ian Goodfellow · 2018
Closest in time.
Meta-tracker: Fast and robust online adaptation for visual object trackers
Eunbyung Park and Alexander C Berg · 2018
Closest in time.
Unsupervised feature learning via non-parametric instance discrimination
Zhirong Wu, Yuanjun Xiong, Stella X Yu, and Dahua Lin · 2018
Closest in time.
Antreas Antoniou and Amos Storkey · 2019
Closest in time.