Leveraging Heterogeneous Contrastive Learning to Train Powerful Foundation Models Across Diverse Data and Tasks

Heterogeneous contrastive learning is a powerful approach to train large-scale foundation models that can effectively handle diverse data sources and tasks without relying on labeled data. By leveraging contrastive learning to model view heterogeneity and task heterogeneity, these foundation models can learn compact and high-quality representations that generalize well across a wide range of applications.