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Rethinking Class-Balanced Methods for Long-Tailed Visual Recognition
  from a Domain Adaptation Perspective

Rethinking Class-Balanced Methods for Long-Tailed Visual Recognition from a Domain Adaptation Perspective

24 March 2020
Muhammad Abdullah Jamal
Matthew A. Brown
Ming-Hsuan Yang
Liqiang Wang
Boqing Gong
ArXivPDFHTML

Papers citing "Rethinking Class-Balanced Methods for Long-Tailed Visual Recognition from a Domain Adaptation Perspective"

4 / 54 papers shown
Title
Identifying and Compensating for Feature Deviation in Imbalanced Deep
  Learning
Identifying and Compensating for Feature Deviation in Imbalanced Deep Learning
Han-Jia Ye
Hong-You Chen
De-Chuan Zhan
Wei-Lun Chao
29
99
0
06 Jan 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
338
11,684
0
09 Mar 2017
Range Loss for Deep Face Recognition with Long-tail
Range Loss for Deep Face Recognition with Long-tail
Xiao Zhang
Zhiyuan Fang
Yandong Wen
Zhifeng Li
Yu Qiao
CVBM
237
446
0
28 Nov 2016
Learning Attributes Equals Multi-Source Domain Generalization
Learning Attributes Equals Multi-Source Domain Generalization
Chuang Gan
Tianbao Yang
Boqing Gong
OOD
152
197
0
03 May 2016
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