mlsanityData to 14 Oct 2026

Contrastive Learning and the Emergence of Attributes Associations

arXiv 2302.10763 ↗First posted 12 Feb 2023cs.CV cs.LG cs.NEcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 15 of its 32 references that are in the map. First posted on arXiv 12 Feb 2023.

Builds on

15 of 32 references.

References · 15

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3 more
  1. 2017EMNIST: an extension of MNIST to handwritten letterscited by 213
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Citing papers per year: 2024: 1.

1 paper.

  1. 2024Contrastive Learning and Abstract Concepts: The Case of Natural Numberscited by 0