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Unraveling Meta-Learning: Understanding Feature Representations for
  Few-Shot Tasks

Unraveling Meta-Learning: Understanding Feature Representations for Few-Shot Tasks

17 February 2020
Micah Goldblum
Steven Reich
Liam H. Fowl
Renkun Ni
Valeriia Cherepanova
Tom Goldstein
    SSL
    OffRL
ArXivPDFHTML

Papers citing "Unraveling Meta-Learning: Understanding Feature Representations for Few-Shot Tasks"

17 / 17 papers shown
Title
Imperative Learning: A Self-supervised Neuro-Symbolic Learning Framework for Robot Autonomy
Imperative Learning: A Self-supervised Neuro-Symbolic Learning Framework for Robot Autonomy
Chen Wang
Kaiyi Ji
Junyi Geng
Zhongqiang Ren
Taimeng Fu
...
Yi Du
Qihang Li
Y. Yang
Xiao Lin
Zhipeng Zhao
SSL
76
9
0
28 Jan 2025
Uncertainty Herding: One Active Learning Method for All Label Budgets
Uncertainty Herding: One Active Learning Method for All Label Budgets
Wonho Bae
Gabriel L. Oliveira
Danica J. Sutherland
UQCV
121
0
0
30 Dec 2024
Is Pre-training Truly Better Than Meta-Learning?
Is Pre-training Truly Better Than Meta-Learning?
Brando Miranda
P. Yu
Saumya Goyal
Yu-xiong Wang
Oluwasanmi Koyejo
39
5
0
24 Jun 2023
Meta-Auxiliary Network for 3D GAN Inversion
Meta-Auxiliary Network for 3D GAN Inversion
Bangrui Jiang
Zhenhua Guo
Yujiu Yang
15
3
0
18 May 2023
Open-Set Likelihood Maximization for Few-Shot Learning
Open-Set Likelihood Maximization for Few-Shot Learning
Malik Boudiaf
Etienne Bennequin
Myriam Tami
Antoine Toubhans
Pablo Piantanida
C´eline Hudelot
Ismail Ben Ayed
BDL
18
10
0
20 Jan 2023
Robust Meta-Representation Learning via Global Label Inference and
  Classification
Robust Meta-Representation Learning via Global Label Inference and Classification
Ruohan Wang
Isak Falk
Massimiliano Pontil
C. Ciliberto
33
3
0
22 Dec 2022
Scalable PAC-Bayesian Meta-Learning via the PAC-Optimal Hyper-Posterior:
  From Theory to Practice
Scalable PAC-Bayesian Meta-Learning via the PAC-Optimal Hyper-Posterior: From Theory to Practice
Jonas Rothfuss
Martin Josifoski
Vincent Fortuin
Andreas Krause
33
7
0
14 Nov 2022
Not All Instances Contribute Equally: Instance-adaptive Class
  Representation Learning for Few-Shot Visual Recognition
Not All Instances Contribute Equally: Instance-adaptive Class Representation Learning for Few-Shot Visual Recognition
M. Han
Yibing Zhan
Yong Luo
Bo Du
Han Hu
Yonggang Wen
Dacheng Tao
19
6
0
07 Sep 2022
FS-BAN: Born-Again Networks for Domain Generalization Few-Shot
  Classification
FS-BAN: Born-Again Networks for Domain Generalization Few-Shot Classification
Yunqing Zhao
Ngai-man Cheung
BDL
14
12
0
23 Aug 2022
Improving Meta-Learning Generalization with Activation-Based
  Early-Stopping
Improving Meta-Learning Generalization with Activation-Based Early-Stopping
Simon Guiroy
C. Pal
Gonçalo Mordido
Sarath Chandar
26
3
0
03 Aug 2022
The Curse of Low Task Diversity: On the Failure of Transfer Learning to
  Outperform MAML and Their Empirical Equivalence
The Curse of Low Task Diversity: On the Failure of Transfer Learning to Outperform MAML and Their Empirical Equivalence
Brando Miranda
P. Yu
Yu-xiong Wang
Oluwasanmi Koyejo
23
10
0
02 Aug 2022
Binocular Mutual Learning for Improving Few-shot Classification
Binocular Mutual Learning for Improving Few-shot Classification
Ziqi Zhou
Xi Qiu
Jiangtao Xie
Jianan Wu
Chi Zhang
SSL
11
76
0
27 Aug 2021
Simpler is Better: Few-shot Semantic Segmentation with Classifier Weight
  Transformer
Simpler is Better: Few-shot Semantic Segmentation with Classifier Weight Transformer
Zhihe Lu
Sen He
Xiatian Zhu
Li Zhang
Yi-Zhe Song
Tao Xiang
ViT
169
173
0
06 Aug 2021
A Channel Coding Benchmark for Meta-Learning
A Channel Coding Benchmark for Meta-Learning
Rui Li
Ondrej Bohdal
Rajesh K. Mishra
Hyeji Kim
Da Li
Nicholas D. Lane
Timothy M. Hospedales
12
9
0
15 Jul 2021
Bridging Multi-Task Learning and Meta-Learning: Towards Efficient
  Training and Effective Adaptation
Bridging Multi-Task Learning and Meta-Learning: Towards Efficient Training and Effective Adaptation
Haoxiang Wang
Han Zhao
Bo-wen Li
23
88
0
16 Jun 2021
Improving Few-Shot Learning through Multi-task Representation Learning
  Theory
Improving Few-Shot Learning through Multi-task Representation Learning Theory
Quentin Bouniot
I. Redko
Romaric Audigier
Angélique Loesch
Amaury Habrard
42
10
0
05 Oct 2020
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
252
11,677
0
09 Mar 2017
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