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.

翻译：面对物体呈现时，监督学习方案通常仅给出简洁的标签。而在类似呈现中，人类不仅会给出标签，还会涌现出无数联想，其中相当部分由被呈现物体的属性构成。对比学习是一种基于对物体输入表征施加身份保持变换的半监督学习方案。本研究推测，这些施加的变换不仅保留了被呈现物体的身份，同时也保留了其语义上有意义属性的身份。由此推论，此类对比学习方案的输出表征不仅包含对被呈现物体分类有价值的信息，还包含对任意关注属性存在与否的判断信息。本文展示了验证该思想及推测可行性的仿真结果。