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Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
v1v2v3 (latest)

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

9 March 2017
Chelsea Finn
Pieter Abbeel
Sergey Levine
    OOD
ArXiv (abs)PDFHTML

Papers citing "Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks"

50 / 5,504 papers shown
Title
Generalized Inner Loop Meta-Learning
Generalized Inner Loop Meta-Learning
Jaya Kumar Alageshan
Brandon Amos
A. Verma
Phu Mon Htut
Artem Molchanov
Franziska Meier
Douwe Kiela
Kyunghyun Cho
Soumith Chintala
AI4CE
95
160
0
03 Oct 2019
Is Fast Adaptation All You Need?
Is Fast Adaptation All You Need?
Khurram Javed
Hengshuai Yao
Martha White
17
0
0
03 Oct 2019
An empirical study of pretrained representations for few-shot
  classification
An empirical study of pretrained representations for few-shot classification
Tiago Ramalho
Laura Vana-Gur
P. Filzmoser
VLM
57
6
0
03 Oct 2019
Learning Neural Causal Models from Unknown Interventions
Learning Neural Causal Models from Unknown Interventions
Nan Rosemary Ke
O. Bilaniuk
Anirudh Goyal
Stefan Bauer
Hugo Larochelle
Bernhard Schölkopf
Michael C. Mozer
C. Pal
Yoshua Bengio
CMLOOD
119
170
0
02 Oct 2019
Adversarially Robust Few-Shot Learning: A Meta-Learning Approach
Adversarially Robust Few-Shot Learning: A Meta-Learning Approach
Micah Goldblum
Liam H. Fowl
Tom Goldstein
83
13
0
02 Oct 2019
Distilling Effective Supervision from Severe Label Noise
Distilling Effective Supervision from Severe Label Noise
Zizhao Zhang
Han Zhang
Sercan O. Arik
Honglak Lee
Tomas Pfister
NoLa
39
2
0
01 Oct 2019
Omnipush: accurate, diverse, real-world dataset of pushing dynamics with
  RGB-D video
Omnipush: accurate, diverse, real-world dataset of pushing dynamics with RGB-D video
Maria Bauzá
Ferran Alet
Yen-Chen Lin
Tomas Lozano-Perez
L. Kaelbling
Phillip Isola
Alberto Rodriguez
90
22
0
01 Oct 2019
A Large-scale Study of Representation Learning with the Visual Task
  Adaptation Benchmark
A Large-scale Study of Representation Learning with the Visual Task Adaptation Benchmark
Xiaohua Zhai
J. Puigcerver
Alexander Kolesnikov
P. Ruyssen
C. Riquelme
...
Michael Tschannen
Marcin Michalski
Olivier Bousquet
Sylvain Gelly
N. Houlsby
SSL
97
449
0
01 Oct 2019
Graph convolutional networks for learning with few clean and many noisy
  labels
Graph convolutional networks for learning with few clean and many noisy labels
Ahmet Iscen
Giorgos Tolias
Yannis Avrithis
Ondřej Chum
Cordelia Schmid
SSL
82
19
0
01 Oct 2019
Revisiting Fine-tuning for Few-shot Learning
Revisiting Fine-tuning for Few-shot Learning
Akihiro Nakamura
Tatsuya Harada
121
54
0
01 Oct 2019
Meta-Q-Learning
Meta-Q-Learning
Rasool Fakoor
Pratik Chaudhari
Stefano Soatto
Alex Smola
OffRL
97
149
0
30 Sep 2019
MGHRL: Meta Goal-generation for Hierarchical Reinforcement Learning
MGHRL: Meta Goal-generation for Hierarchical Reinforcement Learning
Haotian Fu
Hongyao Tang
Jianye Hao
Wulong Liu
Chong Chen
60
3
0
30 Sep 2019
Meta-learning algorithms for Few-Shot Computer Vision
Meta-learning algorithms for Few-Shot Computer Vision
Etienne Bennequin
VLM
76
6
0
30 Sep 2019
Chameleon: Learning Model Initializations Across Tasks With Different
  Schemas
Chameleon: Learning Model Initializations Across Tasks With Different Schemas
L. Brinkmeyer
Rafael Rêgo Drumond
Randolf Scholz
Josif Grabocka
Lars Schmidt-Thieme
CLL
61
9
0
30 Sep 2019
Test-Time Training with Self-Supervision for Generalization under
  Distribution Shifts
Test-Time Training with Self-Supervision for Generalization under Distribution Shifts
Yu Sun
Xiaolong Wang
Zhuang Liu
John Miller
Alexei A. Efros
Moritz Hardt
TTAOOD
101
96
0
29 Sep 2019
Neural Embedding Propagation on Heterogeneous Networks
Neural Embedding Propagation on Heterogeneous Networks
Carl Yang
Jieyu Zhang
Jiawei Han
93
20
0
29 Sep 2019
Feature Weighting and Boosting for Few-Shot Segmentation
Feature Weighting and Boosting for Few-Shot Segmentation
Khoi Duc Minh Nguyen
S. Todorovic
120
331
0
28 Sep 2019
Regression Planning Networks
Regression Planning Networks
Danfei Xu
Roberto Martín-Martín
De-An Huang
Yuke Zhu
Silvio Savarese
Li Fei-Fei
55
57
0
28 Sep 2019
Meta Learning with Differentiable Closed-form Solver for Fast Video
  Object Segmentation
Meta Learning with Differentiable Closed-form Solver for Fast Video Object Segmentation
Yu Liu
Lingqiao Liu
Haokui Zhang
S. Hamid Rezatofighi
Ian Reid
VOS
50
9
0
28 Sep 2019
Learning Fast Adaptation with Meta Strategy Optimization
Learning Fast Adaptation with Meta Strategy Optimization
Wenhao Yu
Jie Tan
Yunfei Bai
Erwin Coumans
Sehoon Ha
101
95
0
28 Sep 2019
The Differentiable Cross-Entropy Method
The Differentiable Cross-Entropy Method
Brandon Amos
Denis Yarats
131
54
0
27 Sep 2019
Improving Federated Learning Personalization via Model Agnostic Meta
  Learning
Improving Federated Learning Personalization via Model Agnostic Meta Learning
Yihan Jiang
Jakub Konecný
Keith Rush
Sreeram Kannan
FedML
92
606
0
27 Sep 2019
RLBench: The Robot Learning Benchmark & Learning Environment
RLBench: The Robot Learning Benchmark & Learning Environment
Stephen James
Z. Ma
David Rovick Arrojo
Andrew J. Davison
SSLVLMOffRL
145
563
0
26 Sep 2019
Fast and Effective Adaptation of Facial Action Unit Detection Deep Model
Fast and Effective Adaptation of Facial Action Unit Detection Deep Model
Mihee Lee
Ognjen Rudovic
Vladimir Pavlovic
Maja Pantic
CVBM
30
1
0
26 Sep 2019
Overcoming Data Limitation in Medical Visual Question Answering
Overcoming Data Limitation in Medical Visual Question Answering
Binh Duc Nguyen
Thanh-Toan Do
Binh X. Nguyen
Tuong Khanh Long Do
Erman Tjiputra
Quang-Dieu Tran
MedIm
67
151
0
26 Sep 2019
A Theoretical Analysis of the Number of Shots in Few-Shot Learning
A Theoretical Analysis of the Number of Shots in Few-Shot Learning
Tianshi Cao
M. Law
Sanja Fidler
96
63
0
25 Sep 2019
ES-MAML: Simple Hessian-Free Meta Learning
ES-MAML: Simple Hessian-Free Meta Learning
Xingyou Song
Wenbo Gao
Yuxiang Yang
K. Choromanski
Aldo Pacchiano
Yunhao Tang
165
118
0
25 Sep 2019
Stochastic Prototype Embeddings
Stochastic Prototype Embeddings
Tyler R. Scott
Karl Ridgeway
Michael C. Mozer
BDLUQCV
61
14
0
25 Sep 2019
Wider Networks Learn Better Features
Wider Networks Learn Better Features
D. Gilboa
Guy Gur-Ari
46
7
0
25 Sep 2019
Decoder Choice Network for Meta-Learning
Decoder Choice Network for Meta-Learning
Jialin Liu
Yong Li
Longzhi Yang
Chih-Min Lin
Q. Shen
31
9
0
25 Sep 2019
Multi-task Batch Reinforcement Learning with Metric Learning
Multi-task Batch Reinforcement Learning with Metric Learning
Jiachen Li
Q. Vuong
Shuang Liu
Minghua Liu
K. Ciosek
George Andriopoulos
Henrik I. Christensen
H. Su
OffRL
65
2
0
25 Sep 2019
Tackling Long-Tailed Relations and Uncommon Entities in Knowledge Graph
  Completion
Tackling Long-Tailed Relations and Uncommon Entities in Knowledge Graph Completion
Zihao Wang
K. Lai
Piji Li
Lidong Bing
W. Lam
83
34
0
25 Sep 2019
PAC Reinforcement Learning without Real-World Feedback
PAC Reinforcement Learning without Real-World Feedback
Yuren Zhong
A. Deshmukh
Clayton Scott
58
7
0
23 Sep 2019
Meta-Inverse Reinforcement Learning with Probabilistic Context Variables
Meta-Inverse Reinforcement Learning with Probabilistic Context Variables
Lantao Yu
Tianhe Yu
Chelsea Finn
Stefano Ermon
OffRLBDL
66
72
0
20 Sep 2019
Rapid Learning or Feature Reuse? Towards Understanding the Effectiveness
  of MAML
Rapid Learning or Feature Reuse? Towards Understanding the Effectiveness of MAML
Aniruddh Raghu
M. Raghu
Samy Bengio
Oriol Vinyals
323
648
0
19 Sep 2019
Meta-Neighborhoods
Meta-Neighborhoods
Siyuan Shan
Yang Li
Junier Oliva
142
15
0
18 Sep 2019
Adversarial Attacks and Defenses in Images, Graphs and Text: A Review
Adversarial Attacks and Defenses in Images, Graphs and Text: A Review
Han Xu
Yao Ma
Haochen Liu
Debayan Deb
Hui Liu
Jiliang Tang
Anil K. Jain
AAML
79
680
0
17 Sep 2019
ProtoGAN: Towards Few Shot Learning for Action Recognition
ProtoGAN: Towards Few Shot Learning for Action Recognition
Sai Kumar Dwivedi
Vikram Gupta
Rahul Mitra
Shuaib Ahmed
Arjun Jain
97
96
0
17 Sep 2019
MDP Playground: An Analysis and Debug Testbed for Reinforcement Learning
MDP Playground: An Analysis and Debug Testbed for Reinforcement Learning
Raghunandan Rajan
Jessica Lizeth Borja Diaz
Suresh Guttikonda
Fabio Ferreira
André Biedenkapp
Jan Ole von Hartz
Frank Hutter
143
4
0
17 Sep 2019
MetalGAN: a Cluster-based Adaptive Training for Few-Shot Adversarial
  Colorization
MetalGAN: a Cluster-based Adaptive Training for Few-Shot Adversarial Colorization
Tomaso Fontanini
Eleonora Iotti
Andrea Prati
GAN
70
4
0
17 Sep 2019
Meta Reinforcement Learning for Sim-to-real Domain Adaptation
Meta Reinforcement Learning for Sim-to-real Domain Adaptation
Karol Arndt
Murtaza Hazara
Ali Ghadirzadeh
Ville Kyrki
173
106
0
16 Sep 2019
State Representation Learning from Demonstration
State Representation Learning from Demonstration
Astrid Merckling
Michael Pearce
Loic Cressot
Stéphane Doncieux
Matthias Poloczek
OffRL
78
8
0
15 Sep 2019
GradNet: Gradient-Guided Network for Visual Object Tracking
GradNet: Gradient-Guided Network for Visual Object Tracking
Peixia Li
Boyu Chen
Wanli Ouyang
Dong Wang
Xiaoyun Yang
Huchuan Lu
84
238
0
15 Sep 2019
Torchmeta: A Meta-Learning library for PyTorch
Torchmeta: A Meta-Learning library for PyTorch
T. Deleu
Tobias Würfl
Mandana Samiei
Joseph Paul Cohen
Yoshua Bengio
OffRL
74
85
0
14 Sep 2019
Meta-Learning for Few-Shot Time Series Classification
Meta-Learning for Few-Shot Time Series Classification
Jyoti Narwariya
Pankaj Malhotra
Lovekesh Vig
Gautam M. Shroff
T. Vishnu
158
60
0
13 Sep 2019
Rethinking Zero-Shot Learning: A Conditional Visual Classification
  Perspective
Rethinking Zero-Shot Learning: A Conditional Visual Classification Perspective
Keqin Li
Martin Renqiang Min
Y. Fu
VLM
107
127
0
13 Sep 2019
Differentially Private Meta-Learning
Differentially Private Meta-Learning
Jeffrey Li
M. Khodak
S. Caldas
Ameet Talwalkar
FedML
131
109
0
12 Sep 2019
Modular Meta-Learning with Shrinkage
Modular Meta-Learning with Shrinkage
Yutian Chen
A. Friesen
Feryal M. P. Behbahani
Arnaud Doucet
David Budden
Matthew W. Hoffman
Nando de Freitas
KELMOffRL
112
35
0
12 Sep 2019
Neural Semantic Parsing in Low-Resource Settings with Back-Translation
  and Meta-Learning
Neural Semantic Parsing in Low-Resource Settings with Back-Translation and Meta-Learning
Yibo Sun
Duyu Tang
Nan Duan
Yeyun Gong
Xiaocheng Feng
Bing Qin
Daxin Jiang
NAIMedIm
72
25
0
12 Sep 2019
Learning to Propagate for Graph Meta-Learning
Learning to Propagate for Graph Meta-Learning
Lu Liu
Dinesh Manocha
Guodong Long
Jing Jiang
Chengqi Zhang
112
97
0
11 Sep 2019
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