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Unsupervised Domain Adaptation with Deep Neural-Network

arXiv 2307.05601 ↗First posted 10 Jul 2023cs.CV cs.LGcited by 0

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Abstract (arXiv)

This report contributes to the field of unsupervised domain adaptation by providing an analysis of existing methods, introducing a new approach, and demonstrating the potential for improving visual recognition tasks across different domains. The results of this study open up opportunities for further study and development of advanced methods in the field of domain adaptation.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 14 of its 23 references that are in the map. First posted on arXiv 10 Jul 2023.

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References · 14

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2016The Cityscapes Dataset for Semantic Urban Scene Understandingcited by 4,444
  3. 2014Unsupervised Domain Adaptation by Backpropagationcited by 1,917
  4. 2018Moment Matching for Multi-Source Domain Adaptationcited by 1,331
  5. 2017Deep Hashing Network for Unsupervised Domain Adaptationcited by 1,188
  6. 2016Playing for Data: Ground Truth from Computer Gamescited by 818
  7. 2016Deep Transfer Learning with Joint Adaptation Networkscited by 612
  8. 2017VisDA: The Visual Domain Adaptation Challengecited by 435
  9. 2018A Survey of Unsupervised Deep Domain Adaptationcited by 361
  10. 2020FixBi: Bridging Domain Spaces for Unsupervised Domain Adaptationcited by 124
  11. 2020A survey on domain adaptation theory: learning bounds and theoretical guaranteescited by 109
  12. 2021A Survey of Unsupervised Domain Adaptation for Visual Recognitioncited by 31
2 more
  1. 2016Rademacher Complexity Bounds for a Penalized Multiclass Semi-Supervised Algorithmcited by 15
  2. 2018Theoretical Guarantees of Transfer Learningcited by 5

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