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Few-shot Continual Learning: a Brain-inspired Approach

Few-shot Continual Learning: a Brain-inspired Approach

19 April 2021
Liyuan Wang
Qian Li
Yi Zhong
Jun Zhu
    CLL
ArXiv (abs)PDFHTML

Papers citing "Few-shot Continual Learning: a Brain-inspired Approach"

26 / 26 papers shown
Title
Few-Shot Class-Incremental Learning
Few-Shot Class-Incremental Learning
Xiaoyu Tao
Xiaopeng Hong
Xinyuan Chang
Songlin Dong
Xing Wei
Yihong Gong
CLL
100
413
0
23 Apr 2020
Defining Benchmarks for Continual Few-Shot Learning
Defining Benchmarks for Continual Few-Shot Learning
Antreas Antoniou
Massimiliano Patacchiola
Mateusz Ochal
Amos Storkey
48
38
0
15 Apr 2020
Cognitively-Inspired Model for Incremental Learning Using a Few Examples
Cognitively-Inspired Model for Incremental Learning Using a Few Examples
Ali Ayub
Alan Wagner
CLL
52
3
0
27 Feb 2020
Learning to Continually Learn
Learning to Continually Learn
Shawn L. E. Beaulieu
Lapo Frati
Thomas Miconi
Joel Lehman
Kenneth O. Stanley
Jeff Clune
Nick Cheney
KELMCLL
95
148
0
21 Feb 2020
Incremental Meta-Learning via Indirect Discriminant Alignment
Incremental Meta-Learning via Indirect Discriminant Alignment
Qing Liu
Orchid Majumder
Alessandro Achille
Avinash Ravichandran
Rahul Bhotika
Stefano Soatto
CLL
46
6
0
11 Feb 2020
Meta-Learning Representations for Continual Learning
Meta-Learning Representations for Continual Learning
Khurram Javed
Martha White
KELMCLL
82
321
0
29 May 2019
Edge-labeling Graph Neural Network for Few-shot Learning
Edge-labeling Graph Neural Network for Few-shot Learning
Jongmin Kim
Taesup Kim
Sungwoong Kim
Chang D. Yoo
104
457
0
04 May 2019
A Closer Look at Few-shot Classification
A Closer Look at Few-shot Classification
Wei-Yu Chen
Yen-Cheng Liu
Z. Kira
Y. Wang
Jia-Bin Huang
116
1,770
0
08 Apr 2019
Adaptive Posterior Learning: few-shot learning with a surprise-based
  memory module
Adaptive Posterior Learning: few-shot learning with a surprise-based memory module
Tiago Ramalho
M. Garnelo
BDL
120
77
0
07 Feb 2019
How to train your MAML
How to train your MAML
Antreas Antoniou
Harrison Edwards
Amos Storkey
74
778
0
22 Oct 2018
Incremental Few-Shot Learning with Attention Attractor Networks
Incremental Few-Shot Learning with Attention Attractor Networks
Mengye Ren
Renjie Liao
Ethan Fetaya
R. Zemel
CLL
106
181
0
16 Oct 2018
End-to-End Incremental Learning
End-to-End Incremental Learning
F. M. Castro
M. Marín-Jiménez
Nicolás Guil Mata
Cordelia Schmid
Alahari Karteek
CLL
97
1,160
0
25 Jul 2018
Meta-Learning for Semi-Supervised Few-Shot Classification
Meta-Learning for Semi-Supervised Few-Shot Classification
Mengye Ren
Eleni Triantafillou
S. S. Ravi
Jake C. Snell
Kevin Swersky
J. Tenenbaum
Hugo Larochelle
R. Zemel
SSL
79
1,285
0
02 Mar 2018
Continual Lifelong Learning with Neural Networks: A Review
Continual Lifelong Learning with Neural Networks: A Review
G. I. Parisi
Ronald Kemker
Jose L. Part
Christopher Kanan
S. Wermter
KELMCLL
212
2,901
0
21 Feb 2018
Riemannian Walk for Incremental Learning: Understanding Forgetting and
  Intransigence
Riemannian Walk for Incremental Learning: Understanding Forgetting and Intransigence
Arslan Chaudhry
P. Dokania
Thalaiyasingam Ajanthan
Philip Torr
CLL
114
1,146
0
30 Jan 2018
Memory Aware Synapses: Learning what (not) to forget
Memory Aware Synapses: Learning what (not) to forget
Rahaf Aljundi
F. Babiloni
Mohamed Elhoseiny
Marcus Rohrbach
Tinne Tuytelaars
KELMCLL
90
1,651
0
27 Nov 2017
Measuring Catastrophic Forgetting in Neural Networks
Measuring Catastrophic Forgetting in Neural Networks
Ronald Kemker
Marc McClure
Angelina Abitino
Tyler L. Hayes
Christopher Kanan
CLL
127
722
0
07 Aug 2017
CORe50: a New Dataset and Benchmark for Continuous Object Recognition
CORe50: a New Dataset and Benchmark for Continuous Object Recognition
Vincenzo Lomonaco
Davide Maltoni
186
494
0
09 May 2017
Improved Training of Wasserstein GANs
Improved Training of Wasserstein GANs
Ishaan Gulrajani
Faruk Ahmed
Martín Arjovsky
Vincent Dumoulin
Aaron Courville
GAN
230
9,568
0
31 Mar 2017
Prototypical Networks for Few-shot Learning
Prototypical Networks for Few-shot Learning
Jake C. Snell
Kevin Swersky
R. Zemel
305
8,164
0
15 Mar 2017
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Chelsea Finn
Pieter Abbeel
Sergey Levine
OOD
841
11,961
0
09 Mar 2017
Overcoming catastrophic forgetting in neural networks
Overcoming catastrophic forgetting in neural networks
J. Kirkpatrick
Razvan Pascanu
Neil C. Rabinowitz
J. Veness
Guillaume Desjardins
...
A. Grabska-Barwinska
Demis Hassabis
Claudia Clopath
D. Kumaran
R. Hadsell
CLL
376
7,587
0
02 Dec 2016
iCaRL: Incremental Classifier and Representation Learning
iCaRL: Incremental Classifier and Representation Learning
Sylvestre-Alvise Rebuffi
Alexander Kolesnikov
G. Sperl
Christoph H. Lampert
CLLOOD
171
3,784
0
23 Nov 2016
Matching Networks for One Shot Learning
Matching Networks for One Shot Learning
Oriol Vinyals
Charles Blundell
Timothy Lillicrap
Koray Kavukcuoglu
Daan Wierstra
VLM
380
7,343
0
13 Jun 2016
Unsupervised Representation Learning with Deep Convolutional Generative
  Adversarial Networks
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
Alec Radford
Luke Metz
Soumith Chintala
GANOOD
314
14,032
0
19 Nov 2015
ImageNet Large Scale Visual Recognition Challenge
ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky
Jia Deng
Hao Su
J. Krause
S. Satheesh
...
A. Karpathy
A. Khosla
Michael S. Bernstein
Alexander C. Berg
Li Fei-Fei
VLMObjD
1.7K
39,637
0
01 Sep 2014
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