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The quality of data representation is paramount for the performance of amodel. Recent research has focused on enhancing representation learning byincorporating more information about the intra-sample structures of individualdata points, such as local and global attention. Additionally, researchers haveexplored methods to model the inter-sample relationships, including manifold,contrastive, and discrete representation learning. In this study, we introducea new training loss, which considers both intra-sample structure andinter-sample relationships, leveraging the concept of {\it atoms} to representdata points. This new approach, {\it Atom Modeling}, offers a fresh perspectiveto discretize data representations within a continuous space. Throughexperiments, we demonstrate that Atom Modeling enhances the performance ofexisting models in tasks involving classification and generation, acrossdiverse domains including vision and language. These findings underscore thepotential of Atom Modeling to enhance data representation and improve modellearning, suggesting a promising direction for future research.

Representing Data as Atoms: Unifying Intra- and Inter-Sample Relationship to Discretize Data Representation