mlsanityData to 14 Oct 2026

Unsupervised Domain Adaptation within Deep Foundation Latent Spaces

arXiv 2402.14976 ↗First posted 22 Feb 2024cs.CV cs.AI cs.LGcited by 1

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Abstract (arXiv)

The vision transformer-based foundation models, such as ViT or Dino-V2, are aimed at solving problems with little or no finetuning of features. Using a setting of prototypical networks, we analyse to what extent such foundation models can solve unsupervised domain adaptation without finetuning over the source or target domain. Through quantitative analysis, as well as qualitative interpretations of decision making, we demonstrate that the suggested method can improve upon existing baselines, as well as showcase the limitations of such approach yet to be solved.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 20 of its 23 references that are in the map. First posted on arXiv 22 Feb 2024.

Builds on

20 of 23 references.

References · 20

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8 more
  1. 2017Central Moment Discrepancy (CMD) for Domain-Invariant Representation Learningcited by 192
  2. 2022Revisiting Weakly Supervised Pre-Training of Visual Perception Modelscited by 116
  3. 2022B-cos Networks: Alignment is All We Need for Interpretabilitycited by 88
  4. 2021TransMed: Transformers Advance Multi-modal Medical Image Classificationcited by 86
  5. 2021Delving Deep into the Generalization of Vision Transformers under Distribution Shiftscited by 85
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  7. 2021Adversarial Unsupervised Domain Adaptation with Conditional and Label Shift: Infer, Align and Iteratecited by 43
  8. 2023Towards interpretable-by-design deep learning algorithmscited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026FlashSinkhorn: IO-Aware Entropic Optimal Transport on GPUcited by 11