Measure Less, Know More: Self-Supervised Test-Time Feature Acquisition
ECHO-$k$ is introduced, a task-agnostic and self-supervised learning principle for modality acquisition that uses a deep model's internal pretrained representations as proxy targets that summarize cross-modal information and consistently improves budgeted downstream performance across diverse foundation-model backends.
Eeshaan Jain, L. Bleistein, B. Deplancke et al.
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