mlsanityData to 7 Oct 2026

Unsupervised Domain Adaptation in the Absence of Source Data

arXiv 2007.10233 ↗First posted 20 Jul 2020cs.CV cs.LGcited by 14

Not on the map. Why?

Abstract (arXiv)

Current unsupervised domain adaptation methods can address many types of distribution shift, but they assume data from the source domain is freely available. As the use of pre-trained models becomes more prevalent, it is reasonable to assume that source data is unavailable. We propose an unsupervised method for adapting a source classifier to a target domain that varies from the source domain along natural axes, such as brightness and contrast. Our method only requires access to unlabeled target instances and the source classifier. We validate our method in scenarios where the distribution shift involves brightness, contrast, and rotation and show that it outperforms fine-tuning baselines in scenarios with limited labeled data.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Jul 2020.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 4; 2022: 2; 2023: 5; 2024: 1; 2025: 0; 2026: 2.

14 papers.

Sort:

  1. 2023A Comprehensive Survey on Test-Time Adaptation under Distribution Shiftscited by 353
  2. 2021Source-Free Domain Adaptation for Semantic Segmentationcited by 168
  3. 2023A Comprehensive Survey on Source-free Domain Adaptationcited by 104
  4. 2022Source-free Depth for Object Pop-outcited by 52
  5. 2021Data-Free Knowledge Transfer: A Surveycited by 31
  6. 2021Source-free unsupervised domain adaptation for cross-modality abdominal multi-organ segmentationcited by 23
  7. 2021Unsupervised Robust Domain Adaptation without Source Datacited by 19
  8. 2023Source-Free Domain Adaptation with Temporal Imputation for Time Series Datacited by 15
  9. 2023In Search for a Generalizable Method for Source Free Domain Adaptationcited by 12
  10. 2024Evidentially Calibrated Source-Free Time-Series Domain Adaptation with Temporal Imputationcited by 6
  11. 2023Earning Extra Performance from Restrictive Feedbackscited by 3
  12. 2022Polycentric Clustering and Structural Regularization for Source-free Unsupervised Domain Adaptationcited by 2
2 more
  1. 2026Hitting a Moving Target: Test-Time Adaptation for AI Text Detection under Continual Distribution Shiftcited by 1
  2. 2026Temporal-Spectral Alignment with Frequency Adaptation for Source-Free Time-Series Adaptationcited by 0