Relative Representations: Topological and Geometric Perspectives

Relative representations are an established approach to zero-shot model stitching, consisting of a non-trainable transformation of the latent space of a deep neural network. Based on insights of topological and geometric nature, we propose two improvements to relative representations. First, we intr...

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Bibliographic Details
Published in:arXiv.org 2024-09
Main Authors: García-Castellanos, Alejandro, Marchetti, Giovanni Luca, Kragic, Danica, Scolamiero, Martina
Format: Article
Language:English
Subjects:
Artificial neural networks
Clustering
Densification
Permutations
Regularization
Representations
Stitching
Topology
Online Access:Get full text
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Summary:Relative representations are an established approach to zero-shot model stitching, consisting of a non-trainable transformation of the latent space of a deep neural network. Based on insights of topological and geometric nature, we propose two improvements to relative representations. First, we introduce a normalization procedure in the relative transformation, resulting in invariance to non-isotropic rescalings and permutations. The latter coincides with the symmetries in parameter space induced by common activation functions. Second, we propose to deploy topological densification when fine-tuning relative representations, a topological regularization loss encouraging clustering within classes. We provide an empirical investigation on a natural language task, where both the proposed variations yield improved performance on zero-shot model stitching.
ISSN:2331-8422