Bootstrapping Parallel Anchors for Relative Representations

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.

Publication
Tiny Papers @ ICLR 2023
Irene Cannistraci
Irene Cannistraci
PhD Student

I am a Ph.D. student passionate about Artificial Intelligence.

Luca Moschella
Luca Moschella
PhD Student

PhD Student @SapienzaRoma CS | Intern @NVIDIA Toronto Lab | @NNAISENSE

Valentino Maiorca
Valentino Maiorca
PhD Student

PhD student @ Sapienza, University of Rome

Marco Fumero
Marco Fumero
PhD Student
Antonio Norelli
Antonio Norelli
PhD Student

PhD student in AI @ Sapienza University of Rome, CS dep. I love teaching, especially to machines.

Emanuele Rodolà
Emanuele Rodolà
Full Professor