ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2106.11344
  4. Cited By
f-Domain-Adversarial Learning: Theory and Algorithms

f-Domain-Adversarial Learning: Theory and Algorithms

21 June 2021
David Acuna
Guojun Zhang
M. Law
Sanja Fidler
    FedML
    AI4CE
ArXivPDFHTML

Papers citing "f-Domain-Adversarial Learning: Theory and Algorithms"

21 / 21 papers shown
Title
Stein Discrepancy for Unsupervised Domain Adaptation
Stein Discrepancy for Unsupervised Domain Adaptation
Anneke von Seeger
Dongmian Zou
Gilad Lerman
142
0
0
24 Feb 2025
Implicit Class-Conditioned Domain Alignment for Unsupervised Domain
  Adaptation
Implicit Class-Conditioned Domain Alignment for Unsupervised Domain Adaptation
Xiang Jiang
Qicheng Lao
Stan Matwin
Mohammad Havaei
63
112
0
09 Jun 2020
Bridging Theory and Algorithm for Domain Adaptation
Bridging Theory and Algorithm for Domain Adaptation
Yuchen Zhang
Tianle Liu
Mingsheng Long
Michael I. Jordan
78
710
0
11 Apr 2019
Domain Adaptation with Asymmetrically-Relaxed Distribution Alignment
Domain Adaptation with Asymmetrically-Relaxed Distribution Alignment
Yifan Wu
Ezra Winston
Divyansh Kaushik
Zachary Chase Lipton
58
127
0
05 Mar 2019
On Learning Invariant Representation for Domain Adaptation
On Learning Invariant Representation for Domain Adaptation
Haiying Zhao
Rémi Tachet des Combes
Kun Zhang
Geoffrey J. Gordon
OOD
63
157
0
27 Jan 2019
Algorithms and Theory for Multiple-Source Adaptation
Algorithms and Theory for Multiple-Source Adaptation
Judy Hoffman
M. Mohri
Ningshan Zhang
OOD
57
172
0
20 May 2018
A DIRT-T Approach to Unsupervised Domain Adaptation
A DIRT-T Approach to Unsupervised Domain Adaptation
Rui Shu
Hung Bui
Hirokazu Narui
Stefano Ermon
72
621
0
23 Feb 2018
Spectral Normalization for Generative Adversarial Networks
Spectral Normalization for Generative Adversarial Networks
Takeru Miyato
Toshiki Kataoka
Masanori Koyama
Yuichi Yoshida
ODL
155
4,437
0
16 Feb 2018
Maximum Classifier Discrepancy for Unsupervised Domain Adaptation
Maximum Classifier Discrepancy for Unsupervised Domain Adaptation
Kuniaki Saito
Kohei Watanabe
Yoshitaka Ushiku
Tatsuya Harada
98
1,788
0
07 Dec 2017
CyCADA: Cycle-Consistent Adversarial Domain Adaptation
CyCADA: Cycle-Consistent Adversarial Domain Adaptation
Judy Hoffman
Eric Tzeng
Taesung Park
Jun-Yan Zhu
Phillip Isola
Kate Saenko
Alexei A. Efros
Trevor Darrell
139
3,001
0
08 Nov 2017
Deep Hashing Network for Unsupervised Domain Adaptation
Deep Hashing Network for Unsupervised Domain Adaptation
Hemanth Venkateswara
José Eusébio
Shayok Chakraborty
S. Panchanathan
OOD
138
2,041
0
22 Jun 2017
Joint Distribution Optimal Transportation for Domain Adaptation
Joint Distribution Optimal Transportation for Domain Adaptation
Nicolas Courty
Rémi Flamary
Amaury Habrard
A. Rakotomamonjy
OT
OOD
80
564
0
24 May 2017
Generate To Adapt: Aligning Domains using Generative Adversarial
  Networks
Generate To Adapt: Aligning Domains using Generative Adversarial Networks
S. Sankaranarayanan
Yogesh Balaji
Carlos D. Castillo
Rama Chellappa
GAN
OOD
103
651
0
06 Apr 2017
f-GAN: Training Generative Neural Samplers using Variational Divergence
  Minimization
f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization
Sebastian Nowozin
Botond Cseke
Ryota Tomioka
GAN
141
1,655
0
02 Jun 2016
Deep Transfer Learning with Joint Adaptation Networks
Deep Transfer Learning with Joint Adaptation Networks
Mingsheng Long
Hanhua Zhu
Jianmin Wang
Michael I. Jordan
TTA
93
2,457
0
21 May 2016
Deep Residual Learning for Image Recognition
Deep Residual Learning for Image Recognition
Kaiming He
Xinming Zhang
Shaoqing Ren
Jian Sun
MedIm
2.1K
193,814
0
10 Dec 2015
How (not) to Train your Generative Model: Scheduled Sampling,
  Likelihood, Adversary?
How (not) to Train your Generative Model: Scheduled Sampling, Likelihood, Adversary?
Ferenc Huszár
OOD
DiffM
GAN
76
296
0
16 Nov 2015
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
366
9,484
0
28 May 2015
Unsupervised Domain Adaptation by Backpropagation
Unsupervised Domain Adaptation by Backpropagation
Yaroslav Ganin
Victor Lempitsky
OOD
233
6,022
0
26 Sep 2014
Domain Adaptation: Learning Bounds and Algorithms
Domain Adaptation: Learning Bounds and Algorithms
Yishay Mansour
M. Mohri
Afshin Rostamizadeh
288
799
0
19 Feb 2009
Estimating divergence functionals and the likelihood ratio by convex
  risk minimization
Estimating divergence functionals and the likelihood ratio by convex risk minimization
X. Nguyen
Martin J. Wainwright
Michael I. Jordan
216
803
0
04 Sep 2008
1