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De-Confusing Pseudo-Labels in Source-Free Domain Adaptation

De-Confusing Pseudo-Labels in Source-Free Domain Adaptation

3 January 2024
I. Diamant
Amir Rosenfeld
Idan Achituve
Jacob Goldberger
Arnon Netzer
ArXivPDFHTML

Papers citing "De-Confusing Pseudo-Labels in Source-Free Domain Adaptation"

5 / 5 papers shown
Title
Guiding Pseudo-labels with Uncertainty Estimation for Source-free
  Unsupervised Domain Adaptation
Guiding Pseudo-labels with Uncertainty Estimation for Source-free Unsupervised Domain Adaptation
Mattia Litrico
Alessio Del Bue
Pietro Morerio
UQCV
27
47
0
07 Mar 2023
Fast Batch Nuclear-norm Maximization and Minimization for Robust Domain
  Adaptation
Fast Batch Nuclear-norm Maximization and Minimization for Robust Domain Adaptation
Shuhao Cui
Shuhui Wang
Junbao Zhuo
Liang Li
Qingming Huang
Qi Tian
27
18
0
13 Jul 2021
Provably End-to-end Label-Noise Learning without Anchor Points
Provably End-to-end Label-Noise Learning without Anchor Points
Xuefeng Li
Tongliang Liu
Bo Han
Gang Niu
Masashi Sugiyama
NoLa
106
119
0
04 Feb 2021
Source Data-absent Unsupervised Domain Adaptation through Hypothesis
  Transfer and Labeling Transfer
Source Data-absent Unsupervised Domain Adaptation through Hypothesis Transfer and Labeling Transfer
Jian Liang
Dapeng Hu
Yunbo Wang
R. He
Jiashi Feng
128
249
0
14 Dec 2020
Domain-Adversarial Training of Neural Networks
Domain-Adversarial Training of Neural Networks
Yaroslav Ganin
E. Ustinova
Hana Ajakan
Pascal Germain
Hugo Larochelle
François Laviolette
M. Marchand
Victor Lempitsky
GAN
OOD
149
8,353
0
28 May 2015
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