Unsupervised Domain Adaptation within Deep Foundation Latent Spaces
Dmitry Kangin Plamen Angelov
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.