Contrastive Learning and the Emergence of Attributes Associations

TL;DR

This paper explores how contrastive learning preserves object attributes in representations, enabling the extraction of attribute information alongside object classification, supported by simulation results.

Contribution

It introduces the idea that contrastive learning preserves semantic attributes, not just object identity, and demonstrates this through simulation evidence.

Findings

Contrastive learning representations contain attribute information.

Simulations support the preservation of object attributes.

Potential for attribute-based decision making in learned representations.

Abstract

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