H-SPLID: HSIC-based Saliency Preserving Latent Information Decomposition

Lukas Miklautz, Chengzhi Shi, Andrii Shkabrii, Theodoros Thirimachos Davarakis, Prudence Lam, Claudia Plant, Jennifer Dy, Stratis Ioannidis

Keywords: Hilbert-Schmidt Independence Criterion (HSIC), Salient Feature Learning, Latent Space Separation

TL;DR: H-SPLID learns salient features via latent space decomposition and we provide theoretical guarantees w.r.t. input perturbations.

Abstract: We introduce H-SPLID, a novel algorithm for learning salient feature representations through the explicit decomposition of salient and non-salient features into separate spaces. We show that H-SPLID promotes learning low-dimensional, task-relevant features. We prove that the expected prediction deviation under input perturbations is upper-bounded by the dimension of the salient subspace and the Hilbert-Schmidt Independence Criterion (HSIC) between inputs and representations. This establishes a link between robustness and latent representation compression in terms of the dimensionality and information preserved. Empirical evaluations on image classification tasks show that models trained with H-SPLID primarily rely on salient input components, as indicated by reduced sensitivity to perturbations affecting non-salient features, such as image backgrounds.

Primary Area: General machine learning (supervised, unsupervised, online, active, etc.)

Submission Number: 23809