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：针对某一物体呈现时，监督学习方案通常返回一个精简的标签。而当人类面对类似呈现时，我们同样会给出一个标签，但除此之外，还会涌现出无数关联信息，其中很大一部分涉及该物体的属性。对比学习是一种半监督学习方案，其核心在于对物体输入表征施加恒等保持变换。本文提出猜想：这些变换除了保持物体的身份外，还保留了其语义上有意义的属性的身份。由此推论，对比学习方案的输出表征不仅包含对物体分类有价值的信息，还能为任何感兴趣属性的存在与否提供决策依据。本文通过仿真结果验证了这一思想及猜想的可行性。