SiamQuality: A ConvNet-Based Foundation Model for Imperfect Physiological Signals

arXiv:2404.17667v1 Announce Type: cross
Abstract: Foundation models, especially those using transformers as backbones, have gained significant popularity, particularly in language and language-vision tasks. However, large foundation models are typically trained on high-quality data, which poses a significant challenge, given the prevalence of poor-quality real-world data. This challenge is more pronounced for developing foundation models for physiological data; such data are often noisy, incomplete, or inconsistent. The present work aims to provide a toolset for developing foundation models on physiological data. …

abstract arxiv challenge cs.lg data eess.sp foundation foundation model however language quality quality data tasks transformers type vision world