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：针对物体呈现，监督学习方案通常仅输出一个精简的标签。而人类在类似呈现下虽也能给出标签，但会同时涌现出大量关联信息，其中很大一部分涉及呈现物体的属性。对比学习是一种基于对物体输入表征施加恒等保持变换的半监督学习方案。本文推测，这些相同的变换不仅保持了呈现物体的恒等性，也保持了其语义有意义属性的恒等性。由此推论，此类对比学习方案的输出表征不仅包含对呈现物体分类有价值的信息，还包含对任何感兴趣属性存在与否的判断信息。本文通过仿真结果展示了这一思想及其推测的可行性。