Fetching the paper…
Reading the bibliography…
The focus of recent meta-learning research has been on the development of learning algorithms that can quickly adapt to test time tasks with limited data and low computational cost.
Model compression
Cristian Buciluǎ, Rich Caruana, and Alexandru Niculescu-Mizil · 2006
Earlier work this paper cites.
80 million tiny images: A large data set for nonparametric object and scene recognition
Antonio Torralba, Rob Fergus, and William T. Freeman · 2008
Earlier work this paper cites.
ImageNet: A Large-Scale Hierarchical Image Database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
Earlier work this paper cites.
Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeffrey Dean · 2015
Earlier work this paper cites.
Siamese neural networks for one-shot image recognition
Gregory Koch, Richard Zemel, and Ruslan Salakhutdinov · 2015
Earlier work this paper cites.
Human-level concept learning through probabilistic program induction
Brenden M Lake, Ruslan Salakhutdinov, and Joshua B Tenenbaum · 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.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 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.
Learning to learn: Model regression networks for easy small sample learning
Yuxiong Wang and Martial Hebert · 2016
Earlier work this paper cites.
Learning from small sample sets by combining unsupervised meta-training with cnns
Yu-Xiong Wang and Martial Hebert · 2016
Earlier work this paper cites.
Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
Earlier work this paper cites.
A simple neural attentive meta-learner
Nikhil Mishra, Mostafa Rohaninejad, Xi Chen, and Pieter Abbeel · 2017
Earlier work this paper cites.
Rapid adaptation with conditionally shifted neurons
Tsendsuren Munkhdalai, Xingdi Yuan, Soroush Mehri, and Adam Trischler · 2017
Earlier work this paper cites.
Optimization as a model for few-shot learning
Sachin Ravi and Hugo Larochelle · 2017
Earlier work this paper cites.
Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard Zemel · 2017
Earlier work this paper cites.
Few-shot learning through an information retrieval lens
Eleni Triantafillou, Richard S. Zemel, and Raquel Urtasun · 2017
Earlier work this paper cites.
A gift from knowledge distillation: Fast optimization, network minimization and transfer learning
Junho Yim, Donggyu Joo, Jihoon Bae, and Junmo Kim · 2017
Earlier work this paper cites.
Meta-learning with differentiable closed-form solvers
Luca Bertinetto, Joao F Henriques, Philip HS Torr, and Andrea Vedaldi · 2018
Earlier work this paper cites.
Born-again neural networks
Tommaso Furlanello, Zachary Chase Lipton, Michael Tschannen, Laurent Itti, and Anima Anandkumar · 2018
Earlier work this paper cites.
Dynamic few-shot visual learning without forgetting
Spyros Gidaris and Nikos Komodakis · 2018
Cited alongside, same era.
Squeeze-and-excitation networks
Jie Hu, Li Shen, and Gang Sun · 2018
Cited alongside, same era.
On first-order meta-learning algorithms
Alex Nichol, Joshua Achiam, and John Schulman · 2018
Cited alongside, same era.
Tadam: Task dependent adaptive metric for improved few-shot learning
Boris Oreshkin, Pau Rodríguez López, and Alexandre Lacoste · 2018
Cited alongside, same era.
Few-shot image recognition by predicting parameters from activations
Siyuan Qiao, Chenxi Liu, Wei Shen, and Alan L. Yuille · 2018
Cited alongside, same era.
Meta-learning for semi-supervised few-shot classification
Mengye Ren, Sachin Ravi, Eleni Triantafillou, Jake Snell, Kevin Swersky, Josh B. Tenenbaum, Hugo Larochelle, and Richard S. Zemel · 2018
Meta-learning with differentiable convex optimization
Kwonjoon Lee, Subhransu Maji, Avinash Ravichandran, and Stefano Soatto · 2019
Later among the works it cites.
Few-shot learning with global class representations
Aoxue Li, Tiange Luo, Tao Xiang, Weiran Huang, and Liwei Wang · 2019
Later among the works it cites.
Finding task-relevant features for few-shot learning by category traversal
Hongyang Li, David Eigen, Samuel Dodge, Matthew Zeiler, and Xiaogang Wang · 2019
Later among the works it cites.
Few-shot image recognition with knowledge transfer
Zhimao Peng, Zechao Li, Junge Zhang, Yan Li, Guo-Jun Qi, and Jinhui Tang · 2019
Later among the works it cites.
Towards understanding knowledge distillation
Mary Phuong and Christoph Lampert · 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
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Adapted deep embeddings: A synthesis of methods for k-shot inductive transfer learning
Tyler Scott, Karl Ridgeway, and Michael C Mozer · 2018
Cited alongside, same era.
Learning to compare: Relation network for few-shot learning
Flood Sung, Yongxin Yang, Li Zhang, Tao Xiang, Philip HS Torr, and Timothy M Hospedales · 2018
Cited alongside, same era.
Low-shot learning from imaginary data
Yu-Xiong Wang, Ross B. Girshick, Martial Hebert, and Bharath Hariharan · 2018
Cited alongside, same era.
Meta-learning: Learning to learn fast
Lilian Weng · 2018
Cited alongside, same era.
Unsupervised feature learning via non-parametric instance discrimination
Zhirong Wu, Yuanjun Xiong, Stella X Yu, and Dahua Lin · 2018
Cited alongside, same era.
Learning embedding adaptation for few-shot learning
Han-Jia Ye, Hexiang Hu, De-Chuan Zhan, and Fei Sha · 2018
Cited alongside, same era.
Later among the works it cites.
Rapid learning or feature reuse? towards understanding the effectiveness of maml
Aniruddh Raghu, Maithra Raghu, Samy Bengio, and Oriol Vinyals · 2019
Later among the works it cites.
Few-shot learning with embedded class models and shot-free meta training
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.
Yonglong Tian, Dilip Krishnan, and Phillip Isola · 2019
Later among the works it cites.
Contrastive representation distillation
Yonglong Tian, Dilip Krishnan, and Phillip Isola · 2019
Later among the works it cites.
Meta-dataset: A dataset of datasets for learning to learn from few examples
Eleni Triantafillou, Tyler Zhu, Vincent Dumoulin, Pascal Lamblin, Utku Evci, Kelvin Xu, Ross Goroshin, Carles Gelada, Kevin Swersky, Pierre-Antoine Manzagol, et al · 2019
Later among the works it cites.
Parn: Position-aware relation networks for few-shot learning
Ziyang Wu, Yuwei Li, Lihua Guo, and Kui Jia · 2019
Later among the works it cites.
Variational few-shot learning
Jian Zhang, Chenglong Zhao, Bingbing Ni, Minghao Xu, and Xiaokang Yang · 2019
Later among the works it cites.
A new meta-baseline for few-shot learning
Yinbo Chen, Xiaolong Wang, Zhuang Liu, Huijuan Xu, and Trevor Darrell · 2020
Closest in time.
A baseline for few-shot image classification
Guneet Singh Dhillon, Pratik Chaudhari, Avinash Ravichandran, and Stefano Soatto · 2020
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 · 2020
Closest in time.
Self-distillation amplifies regularization in hilbert space
Hossein Mobahi, Mehrdad Farajtabar, and Peter L Bartlett · 2020
Closest in time.
Self-distillation amplifies regularization in hilbert space
Hossein Mobahi, Mehrdad Farajtabar, and Peter L. Bartlett · 2020
Closest in time.
What makes for good views for contrastive learning?
Yonglong Tian, Chen Sun, Ben Poole, Dilip Krishnan, Cordelia Schmid, and Phillip Isola · 2020
Closest in time.