Fetching the paper…
Reading the bibliography…
Meta learning methods have found success when applied to few shot classification problems, in which they quickly adapt to a small number of labeled examples.
Neighbourhood component analysis
Sam Roweis, Geoffrey Hinton, and Ruslan Salakhutdinov · 2004
Earlier work this paper cites.
Learning a nonlinear embedding by preserving class neighbourhood structure
Ruslan Salakhutdinov and Geoff Hinton · 2007
Earlier work this paper cites.
Meta-learning approach to neural network optimization
Pavel Kordík, Jan Koutník, Jan Drchal, Oleg Kovářík, Miroslav Čepek, and Miroslav Šnorek · 2010
Earlier work this paper cites.
Understanding the difficulty of training deep feedforward neural networks
Xavier Glorot and Yoshua Bengio · 2010
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
Earlier work this paper cites.
Fine-grained visual classification of aircraft
Subhransu Maji, Esa Rahtu, Juho Kannala, Matthew Blaschko, and Andrea Vedaldi · 2013
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.
Siamese neural networks for one-shot image recognition
Gregory Koch · 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.
Deep learning for visual understanding: A review
Yanming Guo, Yu Liu, Ard Oerlemans, Songyang Lao, Song Wu, and Michael S Lew · 2016
Earlier work this paper cites.
How to scale distributed deep learning?, 2016
Peter H. Jin, Qiaochu Yuan, Forrest Iandola, and Kurt Keutzer · 2016
Earlier work this paper cites.
Deep learning on small datasets using online image search
Martin Kolář, Michal Hradiš, and Pavel Zemčík · 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.
Meta-learning with memory-augmented neural networks
Adam Santoro, Sergey Bartunov, Matthew Botvinick, Daan Wierstra, and Timothy Lillicrap · 2016
Earlier work this paper cites.
Deep metric learning via lifted structured feature embedding
Hyun Oh Song, Yu Xiang, Stefanie Jegelka, and Silvio Savarese · 2016
Earlier work this paper cites.
Improved deep metric learning with multi-class n-pair loss objective
Kihyuk Sohn · 2016
Earlier work this paper cites.
A survey on deep learning in medical image analysis
Geert Litjens, Thijs Kooi, Babak Ehteshami Bejnordi, Arnaud Arindra Adiyoso Setio, Francesco Ciompi, Mohsen Ghafoorian, Jeroen Awm Van Der Laak, Bram Van Ginneken, and Clara I Sánchez · 2017
Earlier work this paper cites.
Meta-sgd: Learning to learn quickly for few-shot learning, 2017
Zhenguo Li, Fengwei Zhou, Fei Chen, and Hang Li · 2017
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.
Film: Visual reasoning with a general conditioning layer, 2017
Ethan Perez, Florian Strub, Harm de Vries, Vincent Dumoulin, and Aaron Courville · 2017
Earlier work this paper cites.
Attention is all you need, 2017
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
Cited alongside, same era.
Optimization as a model for few-shot learning
Sachin Ravi and Hugo Larochelle · 2017
Cited alongside, same era.
Few-shot learning with graph neural networks, 2017
Victor Garcia and Joan Bruna · 2017
Cited alongside, same era.
Predicting visual exemplars of unseen classes for zero-shot learning
Soravit Changpinyo, Wei-Lun Chao, and Fei Sha · 2017
Cited alongside, same era.
Learned optimizers that scale and generalize, 2017
Olga Wichrowska, Niru Maheswaranathan, Matthew W. Hoffman, Sergio Gomez Colmenarejo, Misha Denil, Nando de Freitas, and Jascha Sohl-Dickstein · 2017
Cited alongside, same era.
Meta-learning and universality: Deep representations and gradient descent can approximate any learning algorithm, 2017
Chelsea Finn and Sergey Levine · 2017
Bayesian model-agnostic meta-learning
Jaesik Yoon, Taesup Kim, Ousmane Dia, Sungwoong Kim, Yoshua Bengio, and Sungjin Ahn · 2018
Later among the works it cites.
Meta-learning probabilistic inference for prediction, 2018
Jonathan Gordon, John Bronskill, Matthias Bauer, Sebastian Nowozin, and Richard E. Turner · 2018
Later among the works it cites.
Meta-gradient reinforcement learning, 2018
Zhongwen Xu, Hado van Hasselt, and David Silver · 2018
Later among the works it cites.
Learning to learn without forgetting by maximizing transfer and minimizing interference, 2018
Matthew Riemer, Ignacio Cases, Robert Ajemian, Miao Liu, Irina Rish, Yuhai Tu, and Gerald Tesauro · 2018
Later among the works it cites.
Deep metric learning with hierarchical triplet loss
Weifeng Ge, Weilin Huang, Dengke Dong, and Matthew R. Scott · 2018
Later among the works it cites.
How to train your MAML
Antreas Antoniou, Harrison Edwards, and Amos Storkey · 2019
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Meta learning shared hierarchies, 2017
Kevin Frans, Jonathan Ho, Xi Chen, Pieter Abbeel, and John Schulman · 2017
Cited alongside, same era.
No fuss distance metric learning using proxies
Yair Movshovitz-Attias, Alexander Toshev, Thomas K Leung, Sergey Ioffe, and Saurabh Singh · 2017
Cited alongside, same era.
Deep metric learning with angular loss
Jian Wang, Feng Zhou, Shilei Wen, Xiao Liu, and Yuanqing Lin · 2017
Cited alongside, same era.
A neural representation of sketch drawings
David Ha and Douglas Eck · 2017
Cited alongside, same era.
From macro to micro expression recognition: Deep learning on small datasets using transfer learning
Min Peng, Zhan Wu, Zhihao Zhang, and Tong Chen · 2018
Cited alongside, same era.
Learning to compare: Relation network for few-shot learning
Flood Sung, Yongxin Yang, Li Zhang, Tao Xiang, Philip H.S. Torr, and Timothy M. Hospedales · 2018
Cited alongside, same era.
Later among the works it cites.
Alpha maml: Adaptive model-agnostic meta-learning, 2019
Harkirat Singh Behl, Atılım Güneş Baydin, and Philip H. S. Torr · 2019
Later among the works it cites.
Es-maml: Simple hessian-free meta learning, 2019
Xingyou Song, Wenbo Gao, Yuxiang Yang, Krzysztof Choromanski, Aldo Pacchiano, and Yunhao Tang · 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, and Hugo Larochelle · 2019
Later among the works it cites.
Rapid learning or feature reuse? Towards understanding the effectiveness of maml, 2019
Aniruddh Raghu, Maithra Raghu, Samy Bengio, and Oriol Vinyals · 2019
Later among the works it cites.
Revisiting local descriptor based image-to-class measure for few-shot learning
Wenbin Li, Lei Wang, Jinglin Xu, Jing Huo, Yang Gao, and Jiebo Luo · 2019
Later among the works it cites.
Dense classification and implanting for few-shot learning
Yann Lifchitz, Yannis Avrithis, Sylvaine Picard, and Andrei Bursuc · 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.
Few-shot learning with meta metric learners, 2019
Yu Cheng, Mo Yu, Xiaoxiao Guo, and Bowen Zhou · 2019
Later among the works it cites.
Repmet: Representative-based metric learning for classification and few-shot object detection
Leonid Karlinsky, Joseph Shtok, Sivan Harary, Eli Schwartz, Amit Aides, Rogerio Feris, Raja Giryes, and Alex M. Bronstein · 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.
Meta-learning without memorization, 2019
Mingzhang Yin, George Tucker, Mingyuan Zhou, Sergey Levine, and Chelsea Finn · 2019
Later among the works it cites.
Task agnostic meta-learning for few-shot learning
Muhammad Abdullah Jamal and Guo-Jun Qi · 2019
Later among the works it cites.
Meta-learning with warped gradient descent, 2019
Sebastian Flennerhag, Andrei A. Rusu, Razvan Pascanu, Francesco Visin, Hujun Yin, and Raia Hadsell · 2019
Later among the works it cites.
Metainit: Initializing learning by learning to initialize
Yann N Dauphin and Samuel Schoenholz · 2019
Later among the works it cites.