arXiv AI
2d ago

Measure Less, Know More: Self-Supervised Test-Time Feature Acquisition

The paper introduces ECHO-$k$, a self-supervised, task-agnostic method for selecting which modalities to acquire at test time in multimodal, high-dimensional learning. By using a deep model’s pretrained representations as proxy targets, ECHO-$k$ learns a reinforcement‑learning policy that sequentially chooses informative modalities, providing theoretical guarantees in a linear setting. Experiments show that ECHO-$k$ consistently improves budgeted downstream performance across various foundation‑model backends, offering a principled approach to cost‑aware test‑time deployment when measurements are expensive or time‑constrained.

By Eeshaan Jain, Linus Bleistein, Bart Deplancke, Charlotte Bunne
arXiv Machine Learning
Jun 10

SynIB: Informational Bottleneck for Maximizing Synergy in Multimodal Learning

arXiv:2606. 09853v1 Announce Type: new Abstract: A central objective in multimodal learning is to capture synergy: task-relevant information that arises only from the joint use of multiple modalities, and is not available from any single modality alone.

By Konstantinos Kontras, Teodora Gagaleska, Thomas Strypsteen, Christos Chatzichristos, Matthew Blaschko, Maarten De Vos, Paul Pu Liang