Abstract
The use of relative representations for latent embeddings has shown potential in enabling latent space communication and zero-shot model stitching across a wide range of applications. Nevertheless, relative representations rely on a certain amount of parallel anchors to be given as input, which can be impractical to obtain in certain scenarios. To overcome this limitation, we propose an optimization-based method to discover new parallel anchors from a limited number of seeds. Our approach can be used to find semantic correspondence between different domains, align their relative spaces, and achieve competitive results in several tasks.
Type
Publication
Tiny Papers @ ICLR 2023
Irene Cannistraci
Ph.D. Student in Computer Science, Sapienza University of Rome
GLADIA Research Group
Visiting Researcher Student, Helmholtz Munich AIDOS Lab
I am a Ph.D. student in Computer Science passionate about Deep Learning.