Who Cited It

Maximum Classifier Discrepancy for Unsupervised Domain Adaptation

2018 · 2,280 citations · 3 from inside this corpus

Kuniaki Saito, Kohei Watanabe, Yoshitaka Ushiku, Tatsuya Harada

The source holds an abstract for this work, but its best open-access copy is under no open licence, which does not permit us to republish the text. Read it at the source below.

Maximum Classifier Discrepancy for Unsupervised Domain Adaptation (2018)Maximum Classifier Discrepanc…Adam: A Method for Stochastic Optimization (2014)Adam: A Method for Stochastic…Scikit-learn: Machine Learning in Python (2012)Scikit-learn: Machine Learnin…[No title in the source record — DROPS (Schloss Dagstuhl – Leibniz Center for Informatics… (2015)[No title in the source recor…Domain-Adversarial Training of Neural Networks (2017)Domain-Adversarial Training o…Adversarial Discriminative Domain Adaptation (2017)Adversarial Discriminative Do…A theory of learning from different domains (2009)A theory of learning from dif…Deep CORAL: Correlation Alignment for Deep Domain Adaptation (2016)Deep CORAL: Correlation Align…Learning Transferable Features with Deep Adaptation Networks (2015)Learning Transferable Feature…Unsupervised Domain Adaptation by Backpropagation (2014)Unsupervised Domain Adaptatio…Deep Domain Confusion: Maximizing for Domain Invariance (2014)Deep Domain Confusion: Maximi…Analysis of Representations for Domain Adaptation (2007)Analysis of Representations f…Return of Frustratingly Easy Domain Adaptation (2016)Return of Frustratingly Easy …Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks (2017)Unsupervised Pixel-Level Doma…Deep visual domain adaptation: A survey (2018)Deep visual domain adaptation…Moment Matching for Multi-Source Domain Adaptation (2019)Moment Matching for Multi-Sou…ADVENT: Adversarial Entropy Minimization for Domain Adaptation in\n Semantic Segmentation (2018)ADVENT: Adversarial Entropy M…
16 of 16 neighbouring works in this corpus. Blue is what this paper cites; orange is what cites it, and a dashed line is one neighbour citing another. Only the largest labels are drawn — every node carries its full title on hover.
this paper works it cites works citing it node size = global citations · hover for the full title

What this paper cites, inside the corpus

PaperYearCited
Adam: A Method for Stochastic Optimization201484,698
Scikit-learn: Machine Learning in Python201264,025
[No title in the source record — DROPS (Schloss Dagstuhl – Leibniz Center for Informatics…201550,396
Domain-Adversarial Training of Neural Networks20177,702
Adversarial Discriminative Domain Adaptation20175,087
A theory of learning from different domains20093,607
Deep CORAL: Correlation Alignment for Deep Domain Adaptation20163,286
Learning Transferable Features with Deep Adaptation Networks20152,839
Unsupervised Domain Adaptation by Backpropagation20142,608
Deep Domain Confusion: Maximizing for Domain Invariance20142,354
Analysis of Representations for Domain Adaptation20072,044
Return of Frustratingly Easy Domain Adaptation20161,910
Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks20171,634

What cites it, inside the corpus

PaperYearCited
Deep visual domain adaptation: A survey20182,232
Moment Matching for Multi-Source Domain Adaptation20191,643
ADVENT: Adversarial Entropy Minimization for Domain Adaptation in\n Semantic Segmentation20181,444

Links

DOI · OpenAlex record

Topics

Domain Adaptation and Few-Shot LearningComputer Science
COVID-19 diagnosis using AIMedicine
Artificial Intelligence in Healthcare and EducationMedicine

Is this record sound?

complete

Nothing in this record contradicts itself and no field we check is missing.

  • supports4 author record(s) attached.
  • supports63 reference(s) recorded.
  • supportsThe DOI's year agrees with the publication year.
  • supportsA title is present.

Provenance

Everything above was read from one stored OpenAlex payload, fetched 2026-09-04T03:58:50+00:00.

sha256 86a6d6715ff540a0…