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Few and Fewer: Learning Better from Few Examples Using Fewer Base Classes

Few and Fewer: Learning Better from Few Examples Using Fewer Base Classes

Raphael Lafargue
Yassir Bendou
Bastien Pasdeloup
J. Diguet
Ian Reid
Vincent Gripon
Jack Valmadre
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Papers citing "Few and Fewer: Learning Better from Few Examples Using Fewer Base Classes"

5 / 5 papers shown
Title
RankMe: Assessing the downstream performance of pretrained self-supervised representations by their rank
RankMe: Assessing the downstream performance of pretrained self-supervised representations by their rank
Q. Garrido
Randall Balestriero
Laurent Najman
Yann LeCun
SSL
46
71
0
05 Oct 2022
CrossTransformers: spatially-aware few-shot transfer
CrossTransformers: spatially-aware few-shot transfer
Carl Doersch
Ankush Gupta
Andrew Zisserman
ViT
201
328
0
22 Jul 2020
Cross Attention Network for Few-shot Classification
Cross Attention Network for Few-shot Classification
Rui Hou
Hong Chang
Bingpeng Ma
Shiguang Shan
Xilin Chen
202
627
0
17 Oct 2019
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
243
11,568
0
09 Mar 2017
Borrowing Treasures from the Wealthy: Deep Transfer Learning through Selective Joint Fine-tuning
Borrowing Treasures from the Wealthy: Deep Transfer Learning through Selective Joint Fine-tuning
Weifeng Ge
Yizhou Yu
86
232
0
28 Feb 2017