2 citations · 2 across the 2 of their papers we have counts for
2 papers
cs.CV2024
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 netw…
cs.LG2023★ 2 cited
Towards interpretable-by-design deep learning algorithms
Plamen Angelov, Dmitry Kangin, Ziyang Zhang
The proposed framework named IDEAL (Interpretable-by-design DEep learning ALgorithms) recasts the standard supervised classification problem into a function of similarity to a set…