Frustratingly Easy Domain Adaptation

Hal Daumé

doi:https://doi.org/10.48550/arxiv.0907.1815

Frustratingly Easy Domain Adaptation

2009 · arXiv (Cornell University) · 1,396 citations · 10 from inside this corpus

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.

12 of 12 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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Citation edge evidence

Each row is directed from the citing work to the cited work. Source status remains readable without the interactive graph.

A Survey on Transfer Learning→Frustratingly Easy Domain AdaptationLegacy edge — source detail unavailable
A Comprehensive Survey on Transfer Learning→Frustratingly Easy Domain AdaptationLegacy edge — source detail unavailable
A survey of transfer learning→Frustratingly Easy Domain AdaptationLegacy edge — source detail unavailable
A theory of learning from different domains→Frustratingly Easy Domain AdaptationLegacy edge — source detail unavailable
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition→Frustratingly Easy Domain AdaptationLegacy edge — source detail unavailable
Adapting Visual Category Models to New Domains→Frustratingly Easy Domain AdaptationLegacy edge — source detail unavailable
Deep Domain Confusion: Maximizing for Domain Invariance→Frustratingly Easy Domain AdaptationLegacy edge — source detail unavailable
On the Opportunities and Risks of Foundation Models→Frustratingly Easy Domain AdaptationLegacy edge — source detail unavailable
Geodesic flow kernel for unsupervised domain adaptation→Frustratingly Easy Domain AdaptationLegacy edge — source detail unavailable
Return of Frustratingly Easy Domain Adaptation→Frustratingly Easy Domain AdaptationLegacy edge — source detail unavailable
Frustratingly Easy Domain Adaptation→Analysis of Representations for Domain AdaptationLegacy edge — source detail unavailable
Frustratingly Easy Domain Adaptation→Domain adaptation with structural correspondence learningLegacy edge — source detail unavailable
Deep Domain Confusion: Maximizing for Domain Invariance→Adapting Visual Category Models to New DomainsLegacy edge — source detail unavailable
Deep Domain Confusion: Maximizing for Domain Invariance→Analysis of Representations for Domain AdaptationLegacy edge — source detail unavailable
Deep Domain Confusion: Maximizing for Domain Invariance→DeCAF: A Deep Convolutional Activation Feature for Generic Visual RecognitionLegacy edge — source detail unavailable
A theory of learning from different domains→Analysis of Representations for Domain AdaptationLegacy edge — source detail unavailable
Analysis of Representations for Domain Adaptation→Domain adaptation with structural correspondence learningLegacy edge — source detail unavailable
Geodesic flow kernel for unsupervised domain adaptation→Adapting Visual Category Models to New DomainsLegacy edge — source detail unavailable
Geodesic flow kernel for unsupervised domain adaptation→Analysis of Representations for Domain AdaptationLegacy edge — source detail unavailable
Geodesic flow kernel for unsupervised domain adaptation→Domain adaptation with structural correspondence learningLegacy edge — source detail unavailable
Geodesic flow kernel for unsupervised domain adaptation→A Survey on Transfer LearningLegacy edge — source detail unavailable
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition→Adapting Visual Category Models to New DomainsLegacy edge — source detail unavailable
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition→Geodesic flow kernel for unsupervised domain adaptationLegacy edge — source detail unavailable
A Survey on Transfer Learning→Analysis of Representations for Domain AdaptationLegacy edge — source detail unavailable
A Survey on Transfer Learning→Domain adaptation with structural correspondence learningLegacy edge — source detail unavailable
A survey of transfer learning→Adapting Visual Category Models to New DomainsLegacy edge — source detail unavailable
A survey of transfer learning→Geodesic flow kernel for unsupervised domain adaptationLegacy edge — source detail unavailable
A survey of transfer learning→Domain adaptation with structural correspondence learningLegacy edge — source detail unavailable
A survey of transfer learning→A Survey on Transfer LearningLegacy edge — source detail unavailable
Return of Frustratingly Easy Domain Adaptation→Deep Domain Confusion: Maximizing for Domain InvarianceLegacy edge — source detail unavailable
Return of Frustratingly Easy Domain Adaptation→Adapting Visual Category Models to New DomainsLegacy edge — source detail unavailable
Return of Frustratingly Easy Domain Adaptation→Analysis of Representations for Domain AdaptationLegacy edge — source detail unavailable
Return of Frustratingly Easy Domain Adaptation→Geodesic flow kernel for unsupervised domain adaptationLegacy edge — source detail unavailable
Return of Frustratingly Easy Domain Adaptation→DeCAF: A Deep Convolutional Activation Feature for Generic Visual RecognitionLegacy edge — source detail unavailable
Return of Frustratingly Easy Domain Adaptation→Domain adaptation with structural correspondence learningLegacy edge — source detail unavailable
A Comprehensive Survey on Transfer Learning→Deep Domain Confusion: Maximizing for Domain InvarianceLegacy edge — source detail unavailable
A Comprehensive Survey on Transfer Learning→Adapting Visual Category Models to New DomainsLegacy edge — source detail unavailable
A Comprehensive Survey on Transfer Learning→Geodesic flow kernel for unsupervised domain adaptationLegacy edge — source detail unavailable
A Comprehensive Survey on Transfer Learning→Domain adaptation with structural correspondence learningLegacy edge — source detail unavailable
A Comprehensive Survey on Transfer Learning→A Survey on Transfer LearningLegacy edge — source detail unavailable
A Comprehensive Survey on Transfer Learning→A survey of transfer learningLegacy edge — source detail unavailable
A Comprehensive Survey on Transfer Learning→Return of Frustratingly Easy Domain AdaptationLegacy edge — source detail unavailable
On the Opportunities and Risks of Foundation Models→A theory of learning from different domainsLegacy edge — source detail unavailable
On the Opportunities and Risks of Foundation Models→Domain adaptation with structural correspondence learningLegacy edge — source detail unavailable

What this paper cites, inside the corpus

Paper	Year	Cited
Analysis of Representations for Domain Adaptation	2007	2,044
Domain adaptation with structural correspondence learning	2006	1,565

What cites it, inside the corpus

Paper	Year	Cited
A Survey on Transfer Learning	2009	23,670
A Comprehensive Survey on Transfer Learning	2020	6,441
A survey of transfer learning	2016	6,290
A theory of learning from different domains	2009	3,607
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition	2013	3,564
Adapting Visual Category Models to New Domains	2010	2,873
Deep Domain Confusion: Maximizing for Domain Invariance	2014	2,354
On the Opportunities and Risks of Foundation Models	2021	2,265
Geodesic flow kernel for unsupervised domain adaptation	2012	2,219
Return of Frustratingly Easy Domain Adaptation	2016	1,910

Links

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Topics

Topic Modeling	Computer Science
Natural Language Processing Techniques	Computer Science
Multimodal Machine Learning Applications	Computer Science

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complete

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supports1 author record(s) attached.
supports4 reference(s) recorded.
neutralThe DOI carries no year to check against.
supportsA title is present.

Provenance

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