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Beyond Simple Meta-Learning: Multi-Purpose Models for Multi-Domain,
  Active and Continual Few-Shot Learning
v1v2 (latest)

Beyond Simple Meta-Learning: Multi-Purpose Models for Multi-Domain, Active and Continual Few-Shot Learning

Social Science Research Network (SSRN), 2022
13 January 2022
Peyman Bateni
Jarred Barber
Raghav Goyal
Vaden Masrani
Jan-Willem van de Meent
Leonid Sigal
Frank Wood
    BDLVLM
ArXiv (abs)PDFHTML

Papers citing "Beyond Simple Meta-Learning: Multi-Purpose Models for Multi-Domain, Active and Continual Few-Shot Learning"

3 / 3 papers shown
Robust Meta-Representation Learning via Global Label Inference and
  Classification
Robust Meta-Representation Learning via Global Label Inference and ClassificationIEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2022
Ruohan Wang
Isak Falk
Massimiliano Pontil
C. Ciliberto
366
4
0
22 Dec 2022
Queried Unlabeled Data Improves and Robustifies Class-Incremental
  Learning
Queried Unlabeled Data Improves and Robustifies Class-Incremental Learning
Tianlong Chen
Sijia Liu
Shiyu Chang
Lisa Amini
Zinan Lin
CLL
302
7
0
15 Jun 2022
Omni-Training: Bridging Pre-Training and Meta-Training for Few-Shot
  Learning
Omni-Training: Bridging Pre-Training and Meta-Training for Few-Shot Learning
Yang Shu
Zhangjie Cao
Jing Gao
Jianmin Wang
Philip S. Yu
Mingsheng Long
363
15
0
14 Oct 2021
1
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