Despite the success of a number of recent techniques for visual
self-supervised deep learning, there has been limited investigation into the
representations that are ultimately learned. By leveraging recent advances in
the comparison of neural representations, we explore in this direction by
comparing a contrastive self-supervised algorithm to supervision for simple
image data in a common architecture. We find that the methods learn similar
intermediate representations through dissimilar means, and that the
representations diverge rapidly in the final few layers. We investigate this
divergence, finding that these layers strongly fit to their distinct learning
objectives. We also find that the contrastive objective implicitly fits the
supervised objective in intermediate layers, but that the reverse is not true.
Our work particularly highlights the importance of the learned intermediate
representations, and raises critical questions for auxiliary task design.

对比对神经表示的最新技术，研究了对比自我监督算法和简单图像数据在共同架构中的监督学习方法的中间表示的学习，发现这些方法通过不同的方法学习相似的中间表示，但在最后几层中表示迅速发散，旨在突出学习的中间表示的重要性，并提出了辅助任务设计的关键问题。