Who Cited It

Deep Domain Confusion: Maximizing for Domain Invariance

2014 · arXiv (Cornell University) · 2,354 citations · 13 from inside this corpus

Eric Tzeng, Judy Hoffman, Ning Zhang, Kate Saenko, Trevor Darrell

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.

Deep Domain Confusion: Maximizing for Domain Invariance (2014)Deep Domain Confusion: Maximi…DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition (2013)DeCAF: A Deep Convolutional A…Adapting Visual Category Models to New Domains (2010)Adapting Visual Category Mode…Analysis of Representations for Domain Adaptation (2007)Analysis of Representations f…Unsupervised Visual Domain Adaptation Using Subspace Alignment (2013)Unsupervised Visual Domain Ad…Frustratingly Easy Domain Adaptation (2009)Frustratingly Easy Domain Ada…Domain-Adversarial Training of Neural Networks (2017)Domain-Adversarial Training o…A Comprehensive Survey on Transfer Learning (2020)A Comprehensive Survey on Tra…Adversarial Discriminative Domain Adaptation (2017)Adversarial Discriminative Do…Deep CORAL: Correlation Alignment for Deep Domain Adaptation (2016)Deep CORAL: Correlation Align…A Survey on Deep Transfer Learning (2018)A Survey on Deep Transfer Lea…Domain randomization for transferring deep neural networks from simulation to the real wo… (2017)Domain randomization for tran…Learning Transferable Features with Deep Adaptation Networks (2015)Learning Transferable Feature…Unsupervised Domain Adaptation by Backpropagation (2014)Unsupervised Domain Adaptatio…Maximum Classifier Discrepancy for Unsupervised Domain Adaptation (2018)Maximum Classifier Discrepanc…Deep visual domain adaptation: A survey (2018)Deep visual domain adaptation…Return of Frustratingly Easy Domain Adaptation (2016)Return of Frustratingly Easy …Moment Matching for Multi-Source Domain Adaptation (2019)Moment Matching for Multi-Sou…Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks (2017)Unsupervised Pixel-Level Doma…
18 of 18 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.
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What this paper cites, inside the corpus

PaperYearCited
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition20133,564
Adapting Visual Category Models to New Domains20102,873
Analysis of Representations for Domain Adaptation20072,044
Unsupervised Visual Domain Adaptation Using Subspace Alignment20131,400
Frustratingly Easy Domain Adaptation20091,396

What cites it, inside the corpus

PaperYearCited
Domain-Adversarial Training of Neural Networks20177,702
A Comprehensive Survey on Transfer Learning20206,441
Adversarial Discriminative Domain Adaptation20175,087
Deep CORAL: Correlation Alignment for Deep Domain Adaptation20163,286
A Survey on Deep Transfer Learning20182,907
Domain randomization for transferring deep neural networks from simulation to the real wo…20172,890
Learning Transferable Features with Deep Adaptation Networks20152,839
Unsupervised Domain Adaptation by Backpropagation20142,608
Maximum Classifier Discrepancy for Unsupervised Domain Adaptation20182,280
Deep visual domain adaptation: A survey20182,232
Return of Frustratingly Easy Domain Adaptation20161,910
Moment Matching for Multi-Source Domain Adaptation20191,643
Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks20171,634

Links

DOI · Open access copy · OpenAlex record

Topics

Domain Adaptation and Few-Shot LearningComputer Science
Multimodal Machine Learning ApplicationsComputer Science
Human Pose and Action RecognitionComputer Science

Is this record sound?

complete

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

  • supports5 author record(s) attached.
  • supports24 reference(s) recorded.
  • neutralThe DOI carries no year to check against.
  • supportsA title is present.

Provenance

Everything above was read from one stored OpenAlex payload, fetched 2026-09-04T03:58:49+00:00.

sha256 a2172c1bbe00c44b…