New method optimizes data acquisition for multimodal AI under budget constraints

Researchers have introduced Cohort-based Active Modality Acquisition (CAMA), a new method for optimizing the acquisition of additional data modalities in multimodal machine learning under budget constraints. CAMA focuses on test-time, cohort-level acquisition, proposing imputation-based strategies to estimate the utility of acquiring a missing modality for selected samples. Experiments demonstrated CAMA's effectiveness in guiding modality acquisition compared to random or entropy-based methods, with a practical application shown in disease prediction using data from the UK Biobank. AI

影响 Optimizes data acquisition for multimodal models, potentially reducing costs and improving performance in resource-constrained settings.

排序理由 This is a research paper detailing a new method for multimodal machine learning. [lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

• Tillmann Rheude
• UK Biobank

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