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In response to an object presentation, supervised learning schemes generallyrespond with a parsimonious label. Upon a similar presentation we humansrespond again with a label, but are flooded, in addition, by a myriad ofassociations. A significant portion of these consist of the presented objectattributes. Contrastive learning is a semi-supervised learning scheme based onthe application of identity preserving transformations on the object inputrepresentations. It is conjectured in this work that these same appliedtransformations preserve, in addition to the identity of the presented object,also the identity of its semantically meaningful attributes. The corollary ofthis is that the output representations of such a contrastive learning schemecontain valuable information not only for the classification of the presentedobject, but also for the presence or absence decision of any attribute ofinterest. Simulation results which demonstrate this idea and the feasibility ofthis conjecture are presented.

Contrastive Learning and the Emergence of Attributes Associations